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Recent trends in quality control, discrimination and authentication of alcoholic beverages using nondestructive instrumental techniques
Journal Pre-proof Recent trends in quality control, discrimination and authentication of alcoholic beverages using nondestructive instrumental techniques Muhammad Arslan, Haroon Elrasheid Tahir, Muhammad Zareef, Jiyong Shi, Allah Rakha, Muhammad Bilal, Huang Xiaowei, Li Zhihua, Zou Xiaobo PII:
S0924-2244(20)30695-6
DOI:
https://doi.org/10.1016/j.tifs.2020.11.021
Reference:
TIFS 3043
To appear in:
Trends in Food Science & Technology
Received Date: 14 May 2020 Revised Date:
20 October 2020
Accepted Date: 25 November 2020
Please cite this article as: Arslan, M., Tahir, H.E., Zareef, M., Shi, J., Rakha, A., Bilal, M., Xiaowei, H., Zhihua, L., Xiaobo, Z., Recent trends in quality control, discrimination and authentication of alcoholic beverages using nondestructive instrumental techniques, Trends in Food Science & Technology (2021), doi: https://doi.org/10.1016/j.tifs.2020.11.021. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.
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Recent trends in quality control, discrimination and authentication of alcoholic beverages
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using nondestructive instrumental techniques
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Muhammad Arslan1, Haroon Elrasheid Tahir1, Muhammad Zareef1, Jiyong Shi1, Allah
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Rakha2, Muhammad Bilal1, Huang Xiaowei1, Li Zhihua1, Zou Xiaobo*1
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School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013 Zhenjiang, Jiangsu, China.
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National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan.
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*Corresponding author. Tel: +86 511 88780174; Fax: +86 511 88780201
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Email address: [email protected] (Zou Xiaobo)
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Abstract
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Background: The consumption of alcoholic beverages is an integral part of many
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socio-cultural traditions. The existing methods of compositional analysis and quality
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control of alcoholic beverages are not satisfying the burgeoning demands of global
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market due to their high cost and slow turnaround time.
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Scope and approach: The nondestructive instrumental techniques can be applied for
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quality control, discrimination and authentication of alcoholic beverages. The cost of
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analysis, reduction in time, environment friendliness, and non-destructive nature of
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these techniques offers bright prospects for the future.
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Key findings and conclusions: This review aims to highlight the most relevant and
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current information on the use of nondestructive instrumental techniques for
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evaluating quality, discrimination and authentication of alcoholic beverages.
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Moreover, it will serve as a guidance document for large scale production units for
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implementation of these laboratory-based smart techniques. This article also covers
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the challenges faced by the beverage industry which needs to be resolved or
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investigated in future studies.
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Keywords: Alcoholic beverage; Chemometrics; Electronic panel system; Spectroscopic
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technique; Multisensor fusion.
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1. Introduction The alcoholic beverage is a drink containing ethyl alcohol or ethanol, produced by the
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fermentation of grains, fruits and other starches. The matrix of alcoholic beverages is a
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complex mixture of ethanol, water, sugars, organic acids, protein, peptides, phenolics and
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volatile aromatic compounds, all of which contribute to the typical sensory attributes of the
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drink (Cozzolino, 2012; Cynkar, Cozzolino, Dambergs, Janik, & Gishen, 2007; Penza &
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Cassano, 2004; Waterhouse, Sacks, & Jeffery, 2016). The consumption of alcoholic drinks
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plays an imperative role in the socio-cultural traditions of many societies. The global
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alcoholic beverages industry valued in excess of $1 trillion in 2018 and is expected to grow
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by 3% over next 5 years (IWSR, 2018).
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The alcoholic fermentation is a biological process involving several enzymes originating
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from diverse range of microorganisms, especially yeast. During fermentation, sugars
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(glucose, fructose, and sucrose) are transformed into cellular energy, producing carbon
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dioxide and ethyl alcohol as by-products (Cavaglia, et al., 2020; H. Li, et al., 2020). The
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heterogeneity in the composition and quality of finished product is often the result of an
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unstable fermentation. Moreover, consumers, regulators, exporters and produces are in
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demand of cost-effective analytical tools for routine quality control of alcoholic beverages
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(Acevedo, Jiménez, Maldonado, Domínguez, & Narváez, 2007; do Santos, et al., 2019;
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Okaru, et al., 2019). Apart from fermentation process, the quality of alcoholic drinks depends
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mainly on the type of raw material used, soil conditions, climate parameters and agronomic
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practices (Szambelan, Nowak, Szwengiel, & Jeleń, 2020; Veljović, Nikićević, & Nikšić,
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2019). The authenticity of alcoholic beverages is one of the most critical issue in term of
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quality and safety. The authenticity of the drinks is regulated by strict guidelines established
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by national regulatory establishments (Kamiloglu, 2019; Martins, Talhavini, Vieira, Zacca, &
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Braga, 2017). As a result, in a highly competitive environment, the industries are required to
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invest in developing appropriate technologies to improve the quality of alcoholic beverages
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while sustaining growth.
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The quality control of alcoholic drinks can be carried out using two type of methods
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namely, subjective and objective. Subjective approaches are primarily based on human
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judgement of the quality characteristics of the alcoholic beverages (Borràs, et al., 2015; Y.-b.
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Huang, Lan, & Lacey, 2004). These include perception of texture, color, odor, flavor and
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mouthfeel by the consumers or panel of experts (Polášková, Herszage, & Ebeler, 2008).
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Albeit, human assessors may be highly skilled personnel, their opinion may differ owing to 3
their psychological variability (Y.-b. Huang, et al., 2004; Smyth, 2005). The sensory
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evaluation by highly trained panel of experts is also time consuming and susceptible to large
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scale variation. Naturally, such estimation can be biased and subjected to day-to-day
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variations (Jellema, Janssen, Terpstra, de Wijk, & Smilde, 2005; Smyth, 2005). On the other
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hand, objective methods of quality control of alcoholic beverages involve different
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nondestructive instrumental techniques. These methods offer numerous advantages since they
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are reproducible, less erroneous, non-subjective, and above all, are not subject to adaptation
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or fatigue (Ross, 2009; Sáenz-Navajas, Fernández-Zurbano, & Ferreira, 2012; Tan, Li, &
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Jiang, 2015). Keeping in view the complex nature of alcoholic drinks, developing and use of
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nondestructive instrumental methods offer numerous advantages in quality control,
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geographical discrimination and authenticity. However, to be of practical use in alcoholic
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beverage industry, these methods need to be rapid, cost-effective and provide reproducible
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results with continuous operation.
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Mass spectrometry (MS) is a powerful analytical technique used to identify and quantify
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analytes using the mass-to-charge ratio (m/z) of ions generated from a sample (Rifai,
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Horvath, Wittwer, & Hoofnagle, 2018). The analyzers used in such devices range from
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simpler quadrupole time-of-flight to more complex Fourier transform mass analyzer.
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Nowadays, MS can be used in direct mode without chromatography, speeding up analysis
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times, and reducing the complexity. MS coupled with liquid chromatography (LC) or gas
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chromatography (GC) along with its characteristic high specificity and sensitivity is a popular
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choice for analysis of proteins, vitamin, lipid, traces elements, among others (Aebersold &
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Mann, 2016). However, the careful sampling and sample preparation are critical steps for
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precise and accurate measurements of desired analytes. The analyst should be well trained in
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sample collection, handling, analysis as well as appropriate record keeping. In most cases,
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samples undergo preparatory operations prior to analysis i.e. grinding, homogenization,
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filtration, distillation/extraction, concentration, drying (if the desire analyte is to be compared
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on dry-weight basis) or dilution (to decrease analyte concentration within the range of the
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instrument). Eventually, such preparatory steps may lead to loss of analyte owing to
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oxidation from exposure to air or light during grinding or homogenization, volatilization of
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compounds or binding to filtering materials. The distillation/extraction methods are often
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time consuming, require large amount of solvents, and occasionally involve high temperature
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for extraction and distillation, leading to artifactual changes to the sample (Heymann &
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Ebeler, 2016).
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The cost of instrumentation is quite high and may not be affordable for usage in small
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production facilities. Moreover, the understanding of basic principles and applications of MS
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can be critical for analysts entrusted to conform regulatory requirement. The isomers of
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compounds having same m/z may not be distinguished using MS. Furthermore, in high
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resolution MS experiments, several parameters such as capillary voltage, vaporizer
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temperature, needle temperature, and nebulizing gases need to be tuned time to time to get
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precise results (Scigelova & Makarov, 2006). The existing traditional analytical methods used for compositional analysis and quality
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control of alcoholic beverages are not satisfying the global market to meet the growing
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demand of production. Moreover, the lack of skilled analyst which further intensified the
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problem. The need for important information related to chemistry of a substance has
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burdened the laboratories to the extent where analysis repetition is becoming difficult. The
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factors such as minimal sample preparation, low cost of analysis, environmentally
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friendliness and promptness are of paramount importance in the sustainable and modern
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alcoholic beverages industries (Li-Chan, Chalmers, & Griffiths, 2010). In order to rapidly
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respond to the changing demands of both the market and the consumer; it is of extreme
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importance for alcoholic beverage industries to in place the qualitative and quantitative
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means for evaluating the quality control, discrimination and authentication of alcoholic drinks
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through objective methods (Borràs, et al., 2015; Debebe, Redi-Abshiro, & Chandravanshi,
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2017; Pontes, et al., 2006; Ragazzo-Sanchez, Chalier, Chevalier-Lucia, Calderon-Santoyo, &
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Ghommidh, 2009; Wu, Li, et al., 2015).
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During last 20 years, green innovative nondestructive instrumental techniques such as
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UV, Vis, near-infrared (NIR), mid infrared (MIR), Raman, Fluorescence and nuclear
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magnetic resonance spectroscopy along with electronic nose (e-nose), electronic tongue (e-
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tongue) and colorimetric sensor array have displayed great potential to provide solution to
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aforementioned problems (Arslan, Xiaobo, Tahir, et al., 2018; Arslan, Xiaobo, Tahir, Zareef,
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et al., 2019; Arslan, Xiaobo, Xuetao, et al., 2018; Cozzolino, et al., 2008; Tahir, Xiaobo,
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Xiaowei, Jiyong, & Mariod, 2016; Tahir, et al., 2017). These techniques are getting popular
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owing to their simplicity, speed, multiple analysis from single scan, little or no sample
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preparation and environmentally friendly. In addition, multiple sensor fusion has made it
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possible to integrate two or more sensors for innovative results (Arslan, Xiaobo, Tahir,
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Zareef, et al., 2019; Dong, Zhuang, Huang, & Fu, 2009; Men, et al., 2017; Tahir, et al.,
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2017). If well developed, nondestructive instrumental techniques could be utilized to carry
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out quality control through quantitative or qualitative means. 5
Nondestructive instrumental technologies are still being developed and the merits of their
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application are already quite clear. However, the demerits include requirement of traditional
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wet chemistry analysis for compositional parameters of alcoholic beverages, which demands
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personal training and large set of measurements for model calibration. Additionally, the
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interpretation of acquired data require complex mathematical processing (Arslan, Xiaobo,
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Tahir, Xuetao, et al., 2019; Ozaki, McClure, & Christy, 2006; Tahir, et al., 2020; F. Xu, Yu,
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Tesso, Dowell, & Wang, 2013). These drawbacks cannot be overlooked. Nevertheless, the
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new developments in chemometrics and computer programs have proven handy to solve
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some of these issues. On that account, the scope of nondestructive instrumental technologies
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is brighter as it can fulfil the future needs by using suitable chemometric methods. Various
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chemometric techniques have been an integral part of these technologies to perform data
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analysis. The data acquired from these nondestructive instrumental techniques are complex
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carrying a mixture of useful and redundant information and therefore require statistical
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processing to draw meaningful conclusions. The use of chemometric algorithms has made it
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possible to extract meaningful information from the data acquired from these nondestructive
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instrumental techniques.
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The aim of this scientific opinion is to provide the latest knowledge about the application
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of nondestructive instrumental techniques for evaluating compositional analysis, quality
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control, geographical discrimination and authentication of alcoholic beverages. Focus will
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also be laid on the technical challenges encountered during the application of these
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techniques and future perspectives.
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2. Nondestructive instrumental techniques
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2.1. Infrared spectroscopy
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The infrared region of the electromagnetic spectrum covers the wavelength from 100,000
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nm to 780 nm or and split into near infrared (NIR), mid infrared (MIR) and far infrared
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subregions (Baeten & Dardenne, 2002; Osborne, 2006). The NIR spectrum covers the
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wavelength range between 2500-780 nm (4000-12,500 cm-1) offering valuable information
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related to chemical and physical characteristics of sample (Deidda, et al., 2019). In NIR
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spectroscopy, sample absorbs specific frequencies from the light source corresponding to
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overtones and combination bands of vibrational transitions of the molecule mainly of CH,
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OH, CO, and NH groups (Deidda, et al., 2019; Nicolai, et al., 2007). Likewise, MIR region
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covers the wavelength range from 25,000 to 2500 nm (400-4000 cm-1) exhibiting various
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well defined and sharp peaks (Bureau, Cozzolino, & Clark, 2019). Typically, MIR
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spectroscopy is applied to collect the information related to molecular composition. 6
The suitable mode for spectral data collection should be based on the optical
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characteristics of sample. The reflectance mode is the easiest to obtain determination owing
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to executing analysis without contact with sample beside fairly and higher light intensities.
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Albeit, it is exposed to variation in surface characteristics. The transmission cell offers
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advantages of reproducible and accurate spectroscopic determination. In addition, the primary
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drawbacks of this mode often require semi preparation or destructive preparation of the
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sample. Normally, spectral data acquired from transmission mode are considered better
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compared to reflectance mode for analyzing internal disorder of the sample. Besides, light
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penetration intensities of the sample are often low, making it a challenge to obtain precise
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determination of transmission, specifically in environments with higher level of ambient
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light. The pseudo absorbance (A) relative to standard reference material is determined A=
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log(1/R), and A= log(1/T) for reflectance and transmittance spectra, respectively (Tahir, et
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al., 2019).
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2.2. Raman spectroscopy
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Raman spectroscopy is very similar to infrared since it provides information related to
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fundamental chemical bonds within the sample matrix. This technique relies upon the special
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phenomenon named as Raman scattering. In process, when the sample is exposed to incident
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light, a small fraction of light is scattered by the sample. The scattered light differs in
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frequencies from that of incident light. This scattered portion contains the evidences related
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to vibrational band energies of organic matrix and are used to produce a Raman spectrum
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(Bumbrah & Sharma, 2016; Skoog, Holler, & Crouch, 2017; Smith & Dent, 2019). Raman
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technique also delivers evidences regarding chemical bonds within a compound as is
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witnessed in infrared spectroscopy. Evidently, the source of laser in Raman spectroscopy
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might cause fluorescence of molecular compounds, decreasing the sensitivity of this
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technique by affecting the signal to noise ratio.
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Previous reports claimed that specific chemical bonds such as C=O, N-H, and C-H
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produce specific peaks (W. E. Huang, Li, Jarvis, Goodacre, & Banwart, 2010). Thus, Raman
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spectroscopy allows the detection of structural molecules, determination of characteristics
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spectral patterns, type of particular chemical bonds, and the quantitative concentration of
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particular components in organic matrix. It also allowed to conveniently study the molecules
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(liquids and gas), biomolecules and polymers in the sample. The kinetic changes and
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different processes in the structure and on the surfaces of these molecules can also be
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monitored in a short period of time with Raman analytical system (Owen, Notingher, Hill,
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Stevens, & Hench, 2006; Smith & Dent, 2019). The Raman scattering also offered another 7
advantage over analytical technique is its ability to work on samples containing water as a
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major part such as alcoholic beverages (Numata, Iida, & Tanaka, 2011; Owen, et al., 2006).
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Well-designed chemometric algorithms and databases are available for identification of
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Raman spectral data, so both quantitative and qualitative outcomes can be obtained through
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Raman technique (Dörfer, Schumacher, Tarcea, Schmitt, & Popp, 2010). In various studies,
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Raman technique was also used in complement to infrared data (Gao, et al., 2018; Tahir, et
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al., 2017). The configuration of wine fermentation monitoring system coupled with auto-
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calibration Fourier transform Raman spectroscopy has been presented (Fig. 1.). In this
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system, measurement optical probe is sealed by a transparent glass. The light emitted by
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Raman spectroscopy proliferates along the fiber and irradiates through the transparent glass,
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thus exiting the wine sample to produce Raman light. The measurement optical fiber collects
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the Raman light and transmit to the light detection unit of Raman spectroscopy. The reference
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optical path also transmits the other partial light beam and the reference standard of the
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Raman spectrum is excited. Thereafter, the reference optical fiber transmits the Raman
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spectrum of the reference standard to the light detection unit of the Raman spectroscopy.
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2.3. Fluorescence spectroscopy
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The type of electromagnetic spectroscopy that examine sample fluorescence termed as
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fluorescence spectroscopy. This nondestructive instrumental technique involves ultraviolet
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beam of light, that excites the electron present in molecules and typically, but not necessarily,
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cause them to emit visible light. At present, fluorescence components present in alcoholic
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drink offers a great perspective and makes the fluorescence technique highly relevant for
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alcoholic beverage industry. Generally, organic aromatic compounds emit fluorescence with
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planar molecular skeletons and conjugated double bonds (Lakowicz, 2006). The major
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components present in alcoholic beverage ae mostly nonfluorescent. However, the different
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type of minor and trace beverage components that belongs to various chemical classes emit
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fluorescence (Christensen, Nørgaard, Bro, & Engelsen, 2006). The fluorescence properties
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are greatly dependent upon acidity, influence of solvent, environment and presence of other
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compounds (Christensen, et al., 2006; Karoui & Blecker, 2011).
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2.4. Nuclear Magnetic Resonance spectroscopy
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Nuclear Magnetic Resonance (NMR) technique is greatly recognized as an imperative
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tool analysis of food sample since it allows the study of both molecular dynamic and
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chemical composition of liquid, solid or semisolid food matrices (Proietti, et al., 2017). NMR
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technique can be valuable for food matrices characterization in term of classification or 8
identification of geographical origin, quality control and allowing the identification of
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counterfeits. This can be attained by using one of the two methods, either untargeted or
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targeted analysis conferring to specific application. The targeted approach permits the
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capturing of specific markers for authenticity of tested sample (A. Sobolev, Circi, &
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Mannina, 2016). The liquid foods such as alcoholic and non-alcoholic beverages, fully
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solubilized foodstuff i.e. honey, and vegetable oils are very easy to analyze through NMR as
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they do not require specific sample preparation. Thus, high resolution spectra can be obtained
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with no or minimal pretreatment of the tested sample. The difficulties related to the presence
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of signals originating from abundant components i.e. water in beer or wine can be resolved
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using specific pulse sequences to suppress these signals (Proietti, et al., 2017; A. P. Sobolev,
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et al., 2019).
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2.5. Electronic tongue
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The e-tongue is a multisensor system based on various sensor array having limited
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selectivity and uses advanced mathematical data analysis for signal processing through
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multivariate analysis or pattern recognition system (Vlasov, Legin, Rudnitskaya, Di Natale,
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& D'amico, 2005). The sensor array is selective for single or group of similar analytes and
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delivers information related to their quantity. The e-tongue instrumentation makes use of
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electrochemical properties of tested sample. Potentiometry and voltammetry e-tongue
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systems are based on different type of working electrodes which serve as an adequate
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detection system and deliver acceptable response (Ciosek & Wróblewski, 2007; Vlasov, et
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al., 2005).
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The principle of potentiometric sensors is based on the measurement of potential
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(voltage) difference on the interface among the reference electrode and working electrode.
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The selectivity and sensitivity of the sensor relies on the interface composition. The interface
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of potentiometric sensor consists of various type of crystalline compositions, polymeric
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membrane, and electrode materials. The combination of various membranes with different
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sensitivity, selectivity and cross reactivity works together for potentiometric e-tongue system
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(Riul Jr, Dantas, Miyazaki, & Oliveira Jr, 2010).
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Likewise, e-tongue voltammetry is used in analytical chemistry owing to its simplicity,
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versatility, robustness and sensitivity. The principle of voltammetry is based on capture of
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current flowing between counter and working electrodes when a potential pulse is applied
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between them. The electrochemically active compounds present in tested samples are either
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reduced or oxidized based upon type of working electrode and range of potential applied. The
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primary limitation of this system is the limited selectivity owing to presence of 9
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electrochemically active compound in the sample which contribute to the measured signals
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below the applied potential (Bard & Faulkner, 2001). The schematic diagram of e-tongue
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based measurement system has been presented in Fig. 2. The measurement system comprises
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of two arrays of electrodes attached to data acquisition system for the e-tongue and humid e-
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nose. The signal conditioning system consists of filters for rejecting the 50 Hz noise and
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amplifiers with very high impedance. The detail of the system has been reported elsewhere
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(Gil-Sánchez, et al., 2011). [Figure 2 here]
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2.6. Electronic nose The e-nose is a nondestructive instrumental technique that imitates the function of human
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nose and comprises of specific chemical sensors array coupled with suitable pattern
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recognition system for identification of complex or simple odors. It captures the fingerprints
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of volatile organic compounds (VOCs) present in the headspace of alcoholic beverage sample
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using an array of semi selective sensors (Röck, Barsan, & Weimar, 2008). Firstly, complex
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mixture of VOCs presents in headspace interact with appropriate receptors. One odorant
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receptor is sensible to multiple odorants and one odorant is detected by multiple odorant
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receptors. At second stage, the signal generated by the receptors is stored in the database
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followed by the identification of the stored signal. The output of the e-nose can be the
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identity, concentration and characteristics of the odors as perceived by human olfaction. The
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sensitivity for the odorant depends on the sensors attached to the sensor array of e-nose. For
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instance, a specific odorant in the headspace of the tested sample may generate lower
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response in one sensor or otherwise (M Ghasemi-Varnamkhasti, et al., 2011; Martı́, Busto,
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Guasch, & Boqué, 2005; Röck, et al., 2008). The schematic diagram of e-nose based
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measurement system has been presented in Fig. 2.
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The response pattern across the sensors is very important feature for different odorant.
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This identification enables e-nose to distinguish an unidentified odorant from the sensor
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response pattern. The test produces a unique response profile to the odorant spectrum by each
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sensor attached to the sensor array. The response pattern generated at the sensor array is used
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to recognize and characterize the odorant through pattern recognition methods. They are used
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for qualitative or quantitative analysis of different VOCs in alcoholic beverages. The pattern
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recognition method offers an advantage to characterize the complex volatile compounds
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mixtures without identification of individual compounds (Peris & Escuder-Gilabert, 2009).
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2.7. Colorimetric sensor array
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The practical use of human nose as an instrument for odor assessment in alcoholic
347
beverage industry is essentially limited because human olfaction is easily fatigued, subjective
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and disordered. Thus, there is an utmost need for a nondestructive instrumental technique that
349
could mimic the human olfaction for application in alcoholic beverage industry (Xiao-wei,
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Xiao-bo, Ji-yong, Zhi-hua, & Jie-wen, 2018). Previously, various useful applications of e-
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nose technique have been explained but they had limited sensitivity to capture VOCs at lower
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concentration and lacked selectivity to recognize various compounds (Buratti, Ballabio,
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Benedetti, & Cosio, 2007; Cynkar, et al., 2007; J Lozano, Arroyo, Santos, Cabellos, &
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Horrillo, 2008; Jesús Lozano, Santos, Sayago, Gutierrez, & Horrillo, 2004; Ragazzo-
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Sanchez, et al., 2009; M. Xu, Wang, & Gu, 2019). The colorimetric sensory array technology
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offered great perspectives to capture and differentiate complex VOCs. This technique mimics
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human olfaction and produces composite response for individual odorant. The sensor array
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produces a strong chemical interaction between active center and analyte rather than a simple
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physical adsorption, that results in chemo responsive changes of color.
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The colorimetric sensor array technique used for chemo responsive dyes as the main
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sensing unit and these dyes produce different color based on chemical environment. The dye
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selection is the key step in the design of colorimetric sensor array. Usually, the dyes are
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selected based on emission quantum yield, high absorption coefficient, large Stokes shift and
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long emission lifetime. The choice of immobilization procedure and solid support selection
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have greatly influenced the sensitivity, selectivity, dynamic range, stability, calibration and
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response time. The chemo response dyes are immobilized on solid support via entrapment,
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covalent binding, adsorption or ion exchange procedures (LaGasse, et al., 2018; Z. Li, Askim,
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& Suslick, 2018; Zhang & Suslick, 2007). Block diagram of model development using
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smartphone based colorimetric sensor array system has been reported in Fig. 3. The interested
370
reader can refer to a previously published review article for working principle and detailed
371
procedure (Xiao-wei, et al., 2018).
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[Figure 3 here]
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2.8. Combination of Sensors
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The utilization and advancement in multiple sensors have made available more and more
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data for integration of two or more sensor fusion for innovative results. The multiple
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technique involves data fusion approach from two or more sensors to acquire the optimal
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response that otherwise would not be possible through single sensor. In data fusion the sensor
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may be of different or same king. The various sensors have been utilized for quantitative and
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qualitative analysis and each sensor has its own weaknesses and strengths. Thus, integration 11
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of sensor information from multiple sensors can deliver better performance as weakness of
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one sensor can be compensated by the strength of other (Borràs, et al., 2015; Dong, et al.,
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2009). The schematic diagram of model development by mean of spectroscopic systems
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based on different level of data fusion has been presented in Fig. 3. The integration of data acquired from nondestructive instrumental techniques can be
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carried out at three levels namely low-level, mid-level and high-level fusion. Low-level
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fusion is practically simple, built on single chemometric model and captures correlations
387
among variables of different blocks. The limitations include possible predominance of one
388
data over the other and presence of high data volume. These can be overcome by mid-level
389
fusion by extracting or selecting significant feature that allows each block to be treated
390
individually thereby reducing the dimensionality of data. Moreover, mid-level enables
391
interpretation of the results, filters block noise or background information, while, contribution
392
of each discrete block can be easily visualized. Since many preprocessing and combination of
393
feature extraction methods are possible, testing all the combinations makes the mid-level data
394
fusion computationally intensive, cumbersome and difficult to validate. Whereas, high-level
395
data fusion permits to concentrate on particularities of individual techniques. The final
396
declaration is based on fewer variables or values that are precisely embedded in the core
397
information from each technique. In this level of fusion each individual matrix is
398
independently treated and the values captured from inefficient techniques do not affect the
399
robustness of the build models as compared to other levels. Albeit, high-level demands
400
precise preprocessing of the data and the information may be lost if the correlation responses
401
between sources is not considered (L. Huang, Zhao, Chen, & Zhang, 2014; Roussel, Bellon-
402
Maurel, Roger, & Grenier, 2003). The selection and combination of optimum variables
403
extracted from the Raman spectroscopy spectra and attenuated total reflectance infrared
404
spectroscopy spectra accompanying with data fusion model development has been reported
405
(Fig. 4.). The advantage of combining two or more sensors are farfetched, making data fusion
406
very powerful and innovative analytical technique that could provide the solutions to the
407
problems where single sensor fails to accomplish it precisely.
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[Figure 4 here]
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2.9. Chemometrics and data mining
410
The nondestructive instrumental techniques involving characteristic fingerprinting tend to
411
record as many features or compounds as technically possible to get a deeper insight into the
412
composition of the alcoholic beverages. The chemical bonds extending across the organic
413
matrix of alcoholic beverages vibrate at specific frequencies, that are influenced by the shape 12
414
of the molecule, period of the associated vibrational coupling, mass of the constituent atoms,
415
and stiffness of the bonds. A specific vibrational bond absorbs in the spectral region where
416
diatomic molecule exhibit only one bond that may stretch, leading to an increase or decrease
417
in distance between two atoms. However, complex molecule may have several bonds where
418
vibrations can be conjugated leading to two possible vibration modes such as bending or
419
stretching (Karoui, Downey, & Blecker, 2010; Woodcock, Downey, & O'Donnell, 2008).
420
Despite some shortcomings, specific chemical groups can be identified using absorption
421
frequencies, the feature traditionally have been regarded as a pivotal advantage of
422
nondestructive instrumental techniques. These techniques deliver unique spectrum, which
423
may be used as a fingerprint of the alcoholic beverage sample.
424
fingerprints form the basis of various applications of nondestructive instrumental techniques
425
including, discrimination of origin, compositional profiling such as polyphenols,
426
anthocyanins, aroma compounds among others, and identification of unknown contaminants
427
in the alcoholic beverages (Arroyo, et al., 2009; Borràs, et al., 2015; Capone, et al., 2013;
428
Wu, Xu, et al., 2016).
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In addition to the characteristic complexity and overlap in the fingerprint of a sample, the
430
spectrum offers greater challenge of data interpretation and quantification. However, broad
431
overlapping bands may reduce the requirement of using larger wavelength in prediction and
432
routine analysis. Recent developments in computer algorithms and instrumentation have
433
taken the advantage of this complexity, turning nondestructive instrumental techniques into
434
much more powerful and simpler tools to build reliable calibration and prediction models
435
using the unique characteristic fingerprints of alcoholic beverages (Bansal, Chhabra, Rawal,
436
& Sharma, 2014).
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The information present in fingerprints of nondestructive instrumental techniques resides
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in the occurrence intensities, peaks, shapes and band positions. The successful approach of
439
extracting structural information, and qualitative or quantitative evidences from
440
nondestructive instrumental data is to use chemometrics or mathematical analysis (Bansal, et
441
al., 2014; Geladi, 2003; Gemperline, 2006). Thankfully, the powerful chemometric methods
442
have only been made available in recent years, but their use has become an important feature
443
for various application.
444
Since, multivariate processing is scale dependent, data from nondestructive instrumental
445
techniques are mostly preprocessed for noise reduction, and removal of systematic
446
uninformative variations. The data acquired from each technique are specifically treated
447
depending upon their explicit characteristics. Presently, many pre-processing techniques such 13
448
as data enhancement, smoothing, normalization, Savitzky-Golay, straight line, subtraction,
449
derivative transformation, scatter correction, first derivative (1st Dev), second derivative (2nd
450
Dev), standard normal variate (SNV), straight line subtraction (SLS), multiplicative scatter
451
correction (MSC), orthogonal signal correction (OSC), direct orthogonal signal correction
452
(DOSC) and iterative discrete wavelet transform (IDWT) are being used for data analysis. Currently, broad range of techniques are available for data reduction, classification and
454
regression. Principal component analysis (PCA) is commonly used tool for spectral data
455
reduction, compression and visualization. The graphical depiction can often disclose
456
clustering or pattern within a dataset since analogous samples in hyperspace are predicted to
457
discover closeness to each other. Thus, the location of unexpected sample may alert analyst
458
about outliers or unusual samples, which may be deleted from the dataset or reanalyzed prior
459
to further data processing (Arslan, Xiaobo, Shi, et al., 2020; Arslan, Xiaobo, Tahir, et al.,
460
2020; Bansal, et al., 2014). The Principal components (PCs) scores can be used for
461
mathematical modelling to classify or identify geographical origin, authentication or quality
462
control of the sample matrix. Various chemometrics algorithms available for sample
463
discrimination or authentication i.e. linear discriminant analysis (LDA), artificial neural
464
networks (ANN), partial least-squares discriminant analysis (PLS-DA), soft independent
465
modeling of class analogy (SIMCA), hierarchical cluster analysis (HCA), factorial
466
discriminant analysis (FDA), stepwise linear discriminant analysis (SLDA), probabilistic
467
neural network (PNN), radial basis functions
468
discriminate analysis (CDA), radial basis neural networks (RB-NN), support vector machine
469
(SVM), random forests (RF), extreme learning machine (ELM), locally linear embedding
470
(LLE), and canonical discriminate analysis (CDA) are used in discriminant methods (Bansal,
471
et al., 2014; Geladi, 2003; Gemperline, 2006; Shi, et al., 2017; Tahir, Xiaobo, Xiaowei, et al.,
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2016; Zareef, et al., 2020).
(RBF), backpropagation (BP), canonical
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Likewise, it is also imperative to build prediction models of quantitative nature. Recently
474
a wide variety of approaches have been undertaken to account for quality control of alcoholic
475
beverages. Principal component regression (PCR), step multiple linear regression (SMLR),
476
partial least squares (PLS), artificial neural network (ANN), nonparametric algorithm (NPA),
477
response surface regression (RSR), chaotic neural network (KIII), synergy interval-PLS
478
(SiPLS), backward interval-PLS (BiPLS), and genetic algorithm-PLS (GAPLS) are the main
479
methods of quantitative analysis listed in the literature (Arslan, Xiaobo, Tahir, et al., 2018;
480
Arslan, Xiaobo, Tahir, Zareef, et al., 2019; Arslan, Xiaobo, Xuetao, et al., 2018; Tahir,
481
Xiaobo, Jiyong, Mariod, & Wiliam, 2016; Tahir, Xiaobo, Tinting, Jiyong, & Mariod, 2016; 14
482
Zareef, et al., 2018). The interested readers can also refer to other sources for further
483
coverage of procedures and chemometric tools (Bansal, et al., 2014; Gad, El‐Ahmady,
484
Abou‐Shoer, & Al‐Azizi, 2013; Geladi, 2003; Gemperline, 2006; Mehmood, Liland,
485
Snipen, & Sæbø, 2012; Roggo, et al., 2007; Shamsipur, Zare‐Shahabadi, Hemmateenejad, &
486
Akhond, 2006; Wold, Sjöström, & Eriksson, 2001).
487
3. Applications of nondestructive instrumental techniques
488
3.1. Near infrared spectroscopy The quality, authenticity and geographical origin discrimination of alcoholic beverages
490
could also be performed using NIR spectroscopy (Table 1). Correlation between NIR spectral
491
data and gas chromatography was performed for quantification of volatile compounds in
492
Vinho Verde wines. NIR spectroscopy data between the range of 5435-6357 cm-1 was
493
explored for further data processing. PCA and boxplot were used for outlier removal and
494
cluster identification before model development using PLS regression. The acquired results
495
for methanol, diethyl malate, ethyl octanoate, 2-methyl-1-butanol, ethyl acetate, ethyl lactate,
496
3-methyl-1-butanol, diethyl succinate, 2-phenylethanol, and 3-methylbutyl acetate were
497
considered quite good with R2 varying from 0.94 to 0.97 (Genisheva, et al., 2018). Likewise,
498
NIR spectroscopy technique integrated with GC-MS was used for determination of 1-
499
propanol, 1-butanol, and 3-methyl-1-butanol in Dukang base liquor. The combinations of
500
optimal spectral interval were captured for building the model that led to excellent results for
501
three alcohols with R2>95.21 and R2>94.72 for calibration and validation sets, respectively.
502
The RMSEP values were in the range of 0.40 to 1.35 mg/100 mL (Han, Zhang, Li, Li, & Liu,
503
2016). The UV-VIS-NIR spectroscopy was used for the determination of trans-resveratrol,
504
syringic acid, quercetin, epicatechin, catechin, malvin, and oenin in commercial red wines.
505
The prediction model was build using PLS and PCR regression. The PLS built model
506
indicated higher correlation coefficient with RMSECV value in the range of 0.14-0.88 mg L-1
507
(Martelo-Vidal & Vázquez, 2014).
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The possible quantification of different amino acids (arginine, histidine, aspartic acid,
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lysine, threonine, phenylalanine, serine, tyrosine, glutamic acid, leucine, proline, isoleucine,
510
glycine, methionine, alanine, and valine) using NIR spectroscopy has also been investigated
511
in Chinese rice wine. HPLC analysis was performed to get the reference data set. The
512
calibration statistics exhibit rcal>0.94 for all the amino acids studied except arginine, histidine
513
and proline. Similarly, cross validation results were higher than 0.81 for twelve amino acids.
514
The RPD values ranged from 1.04 to 2.77 (Shen, et al., 2010). The ability for in-line
515
determination of rice wine composition using Vis-NIR spectroscopy coupled with LS-SVM 15
was investigated. A circle light fibre spectrometer system was used to capture the spectral
517
data. The LS-SVM built model performed better compared to PLS regression yielding higher
518
rval of 0.872, 0.888 and 0.915 and lower RMSEP of 0.033, 0.146 g L-1, and 0.168% for pH,
519
titratable acidity and alcohol content, respectively (Yu, et al., 2009). The concentrations of
520
calcium, manganese, potassium, phosphorus, boron, magnesium, iron, sodium, and sulphur
521
were measured using Vis-NIR spectroscopy in Australian wines. The higher R2val 0.89, 0.86
522
and, 0.90 and lower SECV 147.6, 0.65 and, 9.80 mg L-1 were observed for potassium, iron
523
and calcium, respectively. For other minerals, intermediate values of R2val and SECV were
524
obtained (Yu, et al., 2009). The feasibility of NIR for quantitation of magnesium, potassium,
525
iron and zinc using PLSR was studied. The magnesium and potassium were well predicted
526
with Rcal of 0.885, and 0.958, respectively. The RMSEC and RMSEP ranged between 3.78 to
527
12.10 mg L-1 and 4.17 to 16.90 mg L-1, respectively (Yu, Zhou, Fu, Xie, & Ying, 2007).
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A hybrid chemometric method integrated with Vis-NIR transmission spectroscopy for
529
quantification of pH and solid content of rice wines was employed. The regression coefficient
530
and x-loading were used to propose the adequate wavelength for pH and soluble solids. The
531
acquired result reported r, RMSEP, and SEP values of 0.94, 0.02 and 0.02 for pH and 0.95,
532
0.17 and 0.16 for soluble solids, respectively (F. Liu, He, Wang, & Pan, 2007). The Vis-NIR
533
technique together with LS-SVM for nondestructive determination of pH, titratable acidity
534
and alcohol strength in colored bottles was also investigated. The prediction accuracy of LS-
535
SVM was superior to that of classical PLS with higher rval of 0.866, 0.942, and 0.960 and
536
lower RMSEP of 0.021, 0.071 g L-1, and 0.115 (%) for pH, titratable acidity and alcohol
537
strength, respectively (Yu, Niu, Ying, & Pan, 2008).
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The enological parameters in rice wine were predicted using LS-SVM and NIR
539
spectroscopy. The radial basis function and first ten PCs acquired from PCA analysis were
540
used as kernel function and input feature subset for built models. LS-SVM was slightly better
541
than that of PLS with higher R2val and lower RMSEP for pH, alcohol content, and titratable
542
acidity (Yu, Lin, et al., 2008). Vis-NIR technique was used to quantify ethanol concentration
543
in bottled rice wine. The PLS model based on original spectra yielded higher Rcal of 0.928
544
and Rval of 0.875 for ethanol prediction. The RMSEC and RMSEP were 0.135 and 0.177 (%,
545
v v-1), respectively (Ying, Yu, Pan, & Lin, 2006). The application of NIR spectroscopy for
546
determination of pH, amino acid, total acid alcoholic degree, ˚Brix and amino acids in rice
547
wine was investigated. The best results were observed for ˚Brix and alcoholic degree with
548
R2cal of 0.93 and 0.96, respectively. The RPD of alcoholic degree was recorded higher than 3,
549
indicative of robust built model. The built models for pH, amino acid and total acid were not 16
550
as good as for alcoholic degree and ˚Brix with R2cal>0.83 and RPD ranging from 1.27 to 1.41
551
(Yu, Ying, Fu, & Lu, 2006). Vis-NIR spectroscopy was used to investigate total acid, total sugar and alcohol content
553
in wine. A modified colony optimization algorithm was proposed for wavelength selection to
554
improve the accuracy of the built models. The results revealed that feature variable or
555
wavelength captured by the proposed algorithm enhanced the prediction performance and
556
robustness of the models. The R values based on ACOPLS model were greater than 0.928
557
with RMSEP ranging between 0.001 to 0.206 for all the parameters (Hu, Yin, Ma, & Liu,
558
2018). The volatile compounds, and fatty acids (decanoic acid, hexanoic acid, and octanoic
559
acid) were determined in apple wine using NIR method. The calibration resulted in full cross
560
validation (R2cv) of 0.8278 for 3,4,5-trimethyl-4-heptanol and 0.8916 for hexanol. Similarly,
561
the predictive ability (R2p) had a value of 0.8811 for 3,4,5-trimethyl-4-heptanol and 0.9184
562
for ethyl hexanoate. The RPD was greater than 2.9 in all the studied compounds. For fatty
563
acids, R2p>0.8939 with RMSECV ranging from 0.295 to 0.793 based on PLS model (Ye,
564
Gao, Li, Yuan, & Yue, 2016). Vis/SW-NIR was applied for determination of pH, alcohol
565
content and titratable acidity in sealed, colored bottles of rice wine. The LS-SVM performed
566
better as compared to PLS model with rval and RMSEP ranging between 0.866 to 0.960 and
567
0.02 to 0.11, respectively (Yu, Niu, Ying, & Pan, 2011). The effective wavelength selection
568
algorithms (SPA, RCA, and ICA) combined with Vis/NIR spectroscopy were evaluated for
569
quantification of soluble solids in beer. Ten efficient wavelengths selected by SPA achieved
570
the optimal results when combined with nonlinear model. The SPA-LS-SVM model yielded
571
excellent performance with higher r 0.9818 and lower RMSEP 0.1628 for soluble solid
572
content (F. Liu, Jiang, & He, 2009).
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In order to ensure the consistency and quality of the final product, effective monitoring of
574
fermentation is a persistent need of the alcoholic beverage industry. The pH, total reducing
575
sugars, amino acids, soluble solids, total esters, and alcohol strength are the most critical
576
parameters indicating the status of fermentation process. NIR spectroscopy as a rapid tool
577
was used to evaluate the chemical parameters during fermentation process. The SiSVM
578
model developed using combination of SiPLS and SVM was compared with classical PLS
579
model. After systematic comparison, it was observed that higher prediction accuracy was
580
observed with SiSVM built model for pH, total reducing sugars, and amino acids. The RPD
581
value for measured chemical parameters ranged from 4.67 to 11.84 (Wu, Long, et al., 2015).
582
The FT-NIR spectroscopy together with PLS regression was used for determination of pH,
583
total esters, total sugars and soluble solids in apple wine. The effective wave numbers were 17
selected using optimization function prior to model development. The optimal built model
585
yielded R2 of 0.92, 0.98, 0.93, and 0.91, and RMSEP of 0.096, 0.021, 0.077 and 0.601 for
586
total esters, total acidity, pH, and soluble solids, respectively. The RPD of all the parameters
587
was recorded higher than 3.65 (Ye, Yue, Yuan, & Li, 2014). The efficacy of NIR to monitor
588
and assess the alcohol strength and titratable acidity during apple wine making has been
589
evaluated. The spectral regions 11,995.4-7498.1 cm-1, and 6101.9-5446.2 cm-1 were selected
590
for titratable acidity and alcohol strength, respectively. The higher Rp2 and lower RMSEP
591
values in prediction set were obtained for titratable acidity (0.973, 0.21 g/L) and alcohol
592
strength (0.993, 4.25 mL/L) (Peng, Ge, Cui, & Zhao, 2016). Red wine was evaluated during
593
fermentation for alcohols (glycerol, and ethanol), sugars (glucose, and fructose) and
594
phenolics (total anthocyanins, total phenolics, and total flavonoids) using FT-NIR
595
spectroscopy. The excellent prediction model was obtained for compositional changes,
596
suggesting that FT-NIR instrumentation could be used for simultaneous and in-line
597
monitoring of red wine making process (Di Egidio, Sinelli, Giovanelli, Moles, & Casiraghi,
598
2010). NIR has also been used to quantify tannins, pigmented polymers, and malvidin-3-
599
glucoside in red wine. The sample were procured from fermentation trial conducted during
600
2001 and 2002 vintage harvest using two grape varieties. A good calibration statistics with
601
R2cal>0.80 and RPD ranging from 1.8 to 5.8 was observed for all the parameters (Cozzolino,
602
et al., 2004). Time driven changes during fermentation of Chinese rice wine were monitored
603
using FT-NIR spectroscopy. Variable selection methods and SVM were used to improve the
604
efficiency of PLS built model. The results revealed that nonlinear models performed better
605
when compared to linear model in prediction of ethanol content and total acid. The SVM
606
model based on GA variables yielded best output with R2pre of 0.94 and 0.97, RMSEP of 3.02
607
g/L and 0.10 g/L, and RPD of 8.7 and 6.1, for ethanol and total acid, respectively (Wu, Xu,
608
Wang, et al., 2015). The quality parameters of another rice wine “Makgeolli” were monitored
609
during fermentation using NIR spectroscopy. From the results, R2p was greater than 0.882
610
with SEP ranged from 0.045% to 1.233% for alcohol, titratable acidity and reducing sugar
611
(Kim & Cho, 2015).
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The discrimination of geographical origins and authenticity of alcoholic beverage was
613
successfully performed using NIR technique. NIR spectroscopy coupled with PLS-DA was
614
used to classify the fermentation stages (0-3, 4-6, 7-9, & 10-20 days). The acquired results
615
revealed that fermentation stages were correctly discriminated with classification rate of
616
92.3% for calibration and 89.7% in prediction set (Wu, Long, et al., 2015). The
617
discrimination of Chinese rice wines from different geographical region was carried out. 18
618
Different wavelength ranges were selected for prediction performance comparison. NIRS
619
spectral data in the range of 1300-1650 nm gave optimal model with 100% classification of
620
wines using PLSR (Yu, et al., 2007). The fermentation stages (0, 2, 5,7 and 30-35 days) were
621
classified using FT-NIR spectroscopy integrated with LDA and algorithm SELECT. The
622
results acquired using LDA correctly classified 87% as average value from initial to final
623
phase (Di Egidio, et al., 2010). NIR spectroscopy was used to discriminate the wine age (1, 3,
624
and 5 years).
625
classification rate ranging from 93.75% to 100% (Yu, Lin, et al., 2008). Chinese rice wines
626
from different geographical region (Shaoxing, non-Shaoxing and Fujian) have been
627
discriminated using NIR spectroscopy to avoid fraudulent practices. The results reported that
628
DPLS methods successfully discriminated the samples with correct classification rate of 97.2
629
and 100%, for calibration and validation set, respectively (Shen, et al., 2012). The
630
authenticity and adulteration of Chinese liquors with and without marked age was
631
investigated using NIR spectroscopy. Higher accuracy (94.9%), sensitivity (93.1%), and
632
specificity (97.9%) were observed using SVM built model (H. Chen, Tan, Wu, Wang, & Zhu,
633
2014). Likewise, Chinese liquors sample of six different flavors, ten brands and 22 kinds
634
were discriminated using Vis/NIR spectroscopy coupled SVM, SIMCA and PCA-LDA
635
models. The results revealed that LDA model based on PCA reported better results with
636
average prediction rate of 95.70% in test set and 98.94% in training set. The correct
637
classification rate for flavor, age, alcohol percentage and brands were all greater than 95% (Z.
638
Li, et al., 2014). The combination of UV-Vis-NIR spectroscopy were also explored to
639
classify Sauvignon blanc wines from New Zeeland and Australia. Evidently, PLS-DA
640
correctly classified 86% of wines from New Zeeland and 73% from Australian regions
641
compared to that of SIMCA model. The misclassified wine samples were procured from
642
South Australia (D Cozzolino, WU Cynkar, N Shah, & PA Smith, 2011a).
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LS-SVM gave better results compared to DA analysis with correct
643
NIR spectroscopy integrated with SIMCA method was applied to classify and verify
644
adulteration of whiskey, rum, brandy and vodka. The proposed method successfully applied
645
to identify the adulteration with 100% accuracy at confidence level of 95% (Pontes, et al.,
646
2006). Likewise, Shiraz wines (vintage 2006) were discriminated for their origin from
647
different Australian regions with overall success rate of 60% using NIR spectroscopy coupled
648
with LDA model (Riovanto, Cynkar, Berzaghi, & Cozzolino, 2011). Vis/NIR spectroscopy
649
was explored to discriminate between the white wines of different varietal origin (Riesling
650
and Chardonnay). The DPLS regression model correctly classified 96% of Chardonnay wines
651
and 100% of Riesling compared to PCR model (Cozzolino, Smyth, & Gishen, 2003). 19
Similarly, successful classification rate of 100% and 84% of the Australian and Spanish
653
Tempranillo wines of different geographical origin was observed using Vis/NIR spectroscopy
654
combined with PLS-DA model (L. Liu, Cozzolino, Cynkar, Gishen, & Colby, 2006). The
655
successful characterization of five aging categories of beer was achieved using NIR
656
spectroscopy. Computational tools such as Fisher weights, PCA, LDA, StepLDA, KNN and
657
GA were used in this study. LDA model based on ten variables chosen by means of SELECT
658
performed better with correct prediction range of 62-86% among different categories
659
(Ghasemi-Varnamkhasti & Forina, 2014). Vis/NIR spectroscopy was employed to classify
660
commercial Riesling wines from France, New Zeeland, Germany and Australia. The results
661
reported that SLDA calibration models correctly classified 86%, 67%, 67% and 87.5% of the
662
Australian, New Zealand, French and German Riesling wines, respectively (L. Liu, et al.,
663
2008). Discrimination of wine within a controlled designation of origin was also performed.
664
Resultantly, Rosal wines were 100% correctly classified via LDA built model and accurate
665
classification (100%) was also achieved for Condado Salnés and Ribeira de Ulla wines
666
(Martelo‐Vidal, Domínguez‐Agis, & Vázquez, 2013). In conclusion, NIR spectroscopy as a
667
rapid and nondestructive tool coupled with chemometric methods could be used for
668
discrimination of origin, ascertain the authenticity, monitor compositional changes during
669
fermentation and quality control of alcoholic beverages at the industrial level.
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[Table 1 here]
3.2. Mid infrared spectroscopy
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Numerous studies have demonstrated that MIR spectroscopy can predict the quality of the
673
drinks, rapidly evaluate the geographical origin and analyze the counterfeit samples of
674
alcoholic beverages (Table 2). Previously, FTIR spectroscopy was explored to analyze the
675
parameters related to phenolic contents of wines. The results revealed that Rp2 of TPI280,
676
Folin-Ciocalteu index, Glories color parameters, and CIELab color parameters were higher
677
than 0.8538 with RMSEP value of less than 2.81, indicative of robust built models (Garcia-
678
Hernandez, Salvo-Comino, Martin-Pedrosa, Garcia-Cabezon, & Rodriguez-Mendez, 2020).
679
Another study attempted to predict total acidity, sugar content and alcohol degree in Italian
680
straw wines through MIR technique. The spectral data correlated with HPLC analysis yielded
681
R2pred greater than 0.92 for all the parameters. The RMSEP ranged from 0.029 g/L to 9.9 g/L,
682
exhibiting suitable strategy for quick and robust assessment of quality parameters of straw
683
wine (Croce, et al., 2020). Likewise, FT-MIR was successfully used to determine the
684
antioxidant capacity and bioactive compounds in Cabernet Sauvignon wines. The PLS1 20
model was indicative of Rv2, and Rc2 ranging from 0.9301 to 0.9502, and 0.9389 to 0.9480,
686
respectively for total phenolic, anthocyanins, tannins, flavonoids, ABTS and DPPH assays
687
(Grijalva-Verdugo, Hernández-Martínez, Meza-Márquez, Gallardo-Velázquez, & Osorio-
688
Revilla, 2018). The ethanol was measured in Ethiopian traditional alcoholic beverages using
689
FT-MIR spectroscopy. The PLS built model reported R2 of 0.999 with 0.1% RMSEP for
690
prediction set indicating robust built model (Debebe, et al., 2017). Similarly, grape derived
691
spirit was analyzed for quality via FTIR-ATR spectroscopy. The PLS built model yielded r2
692
in the range of 97.1-99.4 for methanol, acetaldehyde, fusel alcohol, ethyl acetate, and
693
alcoholic strength. The lower and higher RMSECV was recorded for alcoholic strength
694
(0.37%) and methanol (32.4 g/hL), respectively. The RPD was recorded higher than 2.9 for
695
all the quality parameters (Anjos, Santos, Estevinho, & Caldeira, 2016). A novel analytical
696
method for chloride and sulfate determination in wine was developed using FTIR
697
spectroscopy. The developed PLS model produced results with higher accuracy especially for
698
sulfate with R2p 0.98, RMSEP 0.11 g/L, and RPD 6.3. Similarly, built model for chlorides
699
with enough precision and accuracy yielded R2p 0.83, RMSEP 0.18 g/L, and RPD 2.4 to
700
allow semi quantitative determination (dos Santos, Páscoa, Porto, Cerdeira, & Lopes, 2016).
701
MIR spectroscopy was also explored to predict quality parameters (total phenols,
702
anthocyanin, brix, titratable acidity, pH, color intensity, sugars, and electrical conductivity) in
703
two types of alcoholic beverages, raki and wine. From the results, it was observed that
704
developed PLS model yielded R2cal ranging from 0.71 to 0.99, and R2val from 0.54 to 0.98 for
705
all the parameters. In addition, RMSEP and RMSEC varied between 0.02% to 464.70 ppm,
706
and 0.02% to 523.60 ppm, respectively (Ozturk, Yucesoy, & Ozen, 2012). A comparative
707
study was performed to analyze the alcohol degree, total sugars, non-sugar solids, total acids
708
and pH using NIR and MIR spectroscopy in Chinese rice wine. The results indicated that the
709
performance of MIR spectroscopy was better than that of NIR for total acids (R2v 0.854, and
710
RPD 2.6), alcohol degree (R2v 0.942, and RPD 4.3), non-sugar solids (R2v 0.910, and RPD
711
3.2) and total sugars (R2v 0.974, and RPD 6.0). Besides, NIR was little superior for pH
712
determination with R2v of 0.797 and RPD of 2.3 (Shen, Wu, Wei, Liu, & Tang, 2017). A fast
713
and simple method employed MIR spectroscopy for quantification of specific gravity, ethanol
714
percentage, pH, glucose plus fructose, titratable acidity and volatile acidity in commercial
715
Australian white and red wine. Results from this study demonstrated that PLS built model
716
reported R ranging from 0.65 to 0.99 with SEP recorded between 0.0007-1.35 g/L (D
717
Cozzolino, W Cynkar, N Shah, & P Smith, 2011b). Use of FT-MIR spectroscopy for fast
718
evaluation of sugars and acids was also reported in Chinese rice wine. During calibration,
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various quality parameters were precisely determined with rcal ranging from 0.821 to 0.991.
720
Besides, validation models with rval greater than 0.85 were recorded for most of the
721
parameters. The unsatisfactory prediction was observed for isomaltose and isomaltotriose.
722
The RPD captured was close to or higher than 2.0 for all the studied parameters except
723
isomaltotriose and isomaltose (Shen, Ying, Li, Zheng, & Hu, 2011). Ethanol, sucrose, and
724
tartaric acid was predicted in alcoholic beverages (vodka, gin, rum etc.) using MIR
725
spectroscopy. The built model exhibited good precision and accuracy with higher correlation
726
coefficient and lower RMSEP for all the parameters (Nagarajan, Gupta, Mehrotra, & Bajaj,
727
2006). A new approach was explored using MIR spectroscopy for determination of the
728
attenuation limit of beer samples in combination with PLS. A specific spectral region
729
between 800 and 1200 cm-1 was captured containing information related to limit of
730
attenuation. The model yielded rcal 0.979 with RMSEC of 0.40% for attenuation limit
731
(Castritius, Geier, Jochims, Stahl, & Harms, 2012). FTIR spectral data was explored to
732
evaluate the quality of beer and spirit drinks. For validation set, better accuracy and strong
733
correlation for spirits and beer parameters were observed with R2 falling between 0.90-0.98,
734
and 0.97-0.98, respectively. In addition, parameters like EBC color, bitterness and pH
735
showed lower accuracy and weak correlation, but could be determined semi quantitatively
736
(Lachenmeier, 2007). The amino acids in Chinese rice wine were investigated using FT-IR
737
spectroscopy as a novel and rapid analytical technique. The achieved results indicated that
738
prediction accuracy and precision of GA model based on spectral interval selected by SiPLS
739
outperformed with R2 higher than 0.80 and RPD greater than 2.0, compared to that of PLS
740
model (Wu, Xu, Long, Wang, et al., 2015b). In another study, FT-MIR spectroscopy was
741
successfully applied for prediction of both volatile and total acidity of red wines using IPW-
742
PLS built model based on selected spectral regions (Pizarro, González-Sáiz, Esteban-Díez, &
743
Orio, 2011).
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Monitoring fermentation parameters is the key point in controlling the final quality of
745
alcoholic beverage. Previously, ATR-MIR spectroscopy has been explored to determine the
746
total sugars, ethanol, total acids, and amino nitrogen in Chinese rice wine. The spectral data
747
was combined with efficient algorithms (PLS, iPLS, SVM, and iSVM) to explore the optimal
748
model. From the results, iSVM exhibited better results with R2 higher than 0.93 for prediction
749
set and RMSEP value ranging from 0.02 g/L to 2.35 g/L, revealing an excellent built model
750
(Wu, Xu, Long, Zhang, et al., 2015). In another study, fermentation parameters were
751
monitored using FT-IR spectroscopy in Chinese rice wine. The SiSVM model based on
752
spectral data selected by SiPLS was excellent yielding R2 >0.9311 for calibration set and 22
>0.9195 for prediction set. The RMSEP ranged from 0.03 g L-1 to 1.68 g L-1. The highest
754
RPD value was recorded for total sugars (14.60) and lowest for amino nitrogen (5.36),
755
indicating robustness of built models (Wu, Long, et al., 2015). The concentrations of alcohols
756
(glycerol and ethanol), sugars (glucose and fructose) and phenolic compounds (total
757
anthocyanins, total phenolics, and total flavonoids) were monitored during fermentation in
758
red wine using FT-IR spectroscopy. The HPLC and spectrophotometer data was correlated
759
with spectral information acquired during fermentation process to build reliable model. From
760
the results it was observed that PLS model yielded rcal in the range of 0.96-0.99 with excellent
761
cross validation models for all the parameters (Di Egidio, et al., 2010). The fermentation
762
process of Korean traditional wine “Makgeolli” was monitored rapidly and non-destructively
763
using FT-MIR spectroscopy. The built PLS model reported R2 values of 0.984, 0.983, and
764
0.936 for alcohol, reducing sugars, and titratable acidity, respectively. The SEP were
765
recorded between 0.026 to 0.595% (Kim, et al., 2016). Quantification of phenolic compounds
766
during red winemaking process was also explored using FT-MIR technique. The spectral
767
region selected was between 979 and 2989 cm-1 for each developed model. Good values of
768
performance parameters were recorded with R2cal >0.82 and R2val>0.72. The RPD recorded
769
was between 1.9 to 5.5 based on selected spectral regions (Fragoso, Acena, Guasch, Mestres,
770
& Busto, 2011). An ultra-fast, easy to use and robust method was developed for total
771
phenolic compounds and total antioxidant activity using FTIR spectroscopy during
772
winemaking. It was observed that concentration of these compounds varied at each
773
production stage. The R2cal for calibration model was greater than 0.85 for antioxidant
774
activity and higher than 0.93 for phenolic compounds (Preserova, Ranc, Milde, Kubistova, &
775
Stavek, 2015).
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Previously, wines have been discriminated for their phenolic content using PCA analysis,
777
with first three components accounting for 99.8% of total variance between samples (Garcia-
778
Hernandez, et al., 2020). The fermentation stages (0-3, 4-6, 7-9, & 10-20 days) of Chinese
779
rice wine have also been successfully classified using FT-IR spectroscopy. High
780
classification rate in both calibration set (94%) and prediction set (94.9%) was recorded with
781
PLS-DA built model (Wu, Long, et al., 2015). Likewise, FT-IR spectroscopy was used to
782
monitor the fermentation process (0, 2, 5, 7 and 30–35 days) in red wine. The LDA built
783
model outperformed in correctly discriminating the fermentation process with 100%
784
classification rate in both calibration and prediction sets (Di Egidio, et al., 2010). In India, the
785
discrimination for geographical origin of illicit liquors has been attempted through ATR-
786
FTIR spectroscopy, yielding 76% and 93% accuracy with PCA and LDA models (Yadav & 23
Sharma, 2019). The samples from Fatehgarh sahib, Pathankot, Patiala, Masha and Delhi were
788
reported with 100% classification rate, whereas 75% correct classification rate was recorded
789
in samples from Gurdaspur and Ferozpur. A classification model was also developed for the
790
identification of different types of beers (commercial vs craft beer, ale vs lager etc.). Correct
791
classification rate of 100% was reported in differentiating lager vs ale and craft beer vs
792
commercial sample. The reported results further revealed that dissolved gasses did not
793
interfere in the analysis as overlapping artefacts (Gordon, et al., 2018). Analytical approach
794
was developed for authentication and discrimination of whiskies procured from USA, Ireland
795
and Scotland. The parameters such as time of maturation (2, 3, 6, and 12) and origin were
796
considered for discrimination. The whiskey samples from USA were correctly classified in
797
all the built models, indicative of considerable chemical differences compared to that of
798
Ireland or Scotland whiskey. The unknow samples of whiskey could be identified using the
799
developed approach (Sujka & Koczoń, 2018). A comparative study was performed between
800
NIR and MIR spectroscopy for discrimination of Shiraz wines procured from Australian
801
region. The results revealed better accuracy of MIR for separation of Coonawarra and
802
Western Australia wines. It was also reported that LDA and SIMCA models based on MIR
803
achieved 73% and 95.3% overall correct classification, respectively (Riovanto, et al., 2011).
804
The South African young cultivar wines were discriminated using FTMIR and GC-MS data
805
for volatile compounds. The correct classification rate of 98.3% was achieved through MIR
806
spectral data for red cultivar, whereas combination of volatile compounds and MIR spectra
807
correctly classified 86.8% of white cultivar (Louw, et al., 2009). In another study, analytical
808
tool was developed to capture the counterfeit Scotch whisky samples using MIR
809
spectroscopy. The analysis was carried out to investigate dried residues of whisky and
810
caramel solution via ATR diamond crystal. The PCA analysis correctly classified caramel
811
solutions and whisky samples as either authentic or counterfeit (McIntyre, Bilyk, Nordon,
812
Colquhoun, & Littlejohn, 2011). Similarly, effective strategy for detection of fake liquors was
813
developed using LS-SVM built model with a prediction accuracy >97% (D. Chen, Tan,
814
Huang, Lv, & Li, 2019). The discrimination between Cognacs and other distilled beverages
815
was also achieved with PLS-DA with correct classification rate of 95% in test set (Picque, et
816
al., 2006). A rapid tool was developed for discrimination of organic and non-organic wines
817
sourced from different production systems of Australia. The success of classification was
818
100% and 88% for organic and non-organic production system in white wines, respectively,
819
whereas for red wines the success of classification was 73% for organic and 85% of non-
820
organic (Cozzolino, Holdstock, Dambergs, Cynkar, & Smith, 2009). Conclusively, MIR
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spectroscopy can be used as a rapid tool for quality control, discrimination of geographical
822
region, and identification of counterfeit samples of alcoholic beverages. [Table 2 here]
823 824
3.3. Raman spectroscopy Recently, Raman spectroscopy offers a great capability to achieve quality control in
826
alcoholic beverage industry. The major advantage of Raman spectroscopy is its ability to
827
work perfectly with water containing samples compared to other vibrational spectroscopic
828
techniques. The water in alcoholic beverages has a minute effect on scattering and does not
829
interfere with Raman scattering. The literature survey related to Raman spectroscopy is
830
presented in Table 3. Evidently, Raman spectroscopy is being employed in quality assurance
831
and control investigation of Chinese rice wine. The results demonstrate that prediction
832
accuracy of built models based on spectral intervals selected by SiPLS was greatly improved
833
as evident from R2 greater than 0.90, and RMSEP ranging between 0.05 to 2.40 g/L for
834
ethanol, pH, total sugar and acid content. The RPD recorded was higher than 3 for all the
835
parameters. (Wu, Long, et al., 2016). Similarly, combinations of linear and nonlinear
836
algorithms were explored to investigate the fermentation parameters (ethanol, and glucose) of
837
Chinese rice wine. The results explicated that prediction accuracy of built model based on
838
variables captured by CARS was expressively enhanced. Similarly, nonlinear developed
839
models outperformed in prediction of fermentation parameters. Besides, systematic
840
comparison and discussion, CARS-SVM build model delivered optimal performance in
841
predicting ethanol and glucose concentration (Wu, Xu, Long, Wang, et al., 2015a). In another
842
work, Raman spectroscopy was evaluated and compared with NIR an MIR spectroscopy for
843
assessment of alcohol strength, total acidity, density, total sugars, pH, and volatile acidity in
844
white wines. Results revealed better performance of Raman spectroscopy for determination
845
of pH and total sugar. MIR spectroscopy was considered to be most suitable for total acidity
846
and alcohol strength. The three techniques presented similar result for density, while, none of
847
them presented accurate determination of volatile acidity of white wines (dos Santos, et al.,
848
2018). While, a robust model was developed for ethanol concentration in Chinese rice wine.
849
The achieved results presented higher correlation coefficient and lower error of prediction
850
using dispersive Raman spectroscopy (Yang & Ying, 2011). In another scientific study,
851
ethanol and glucose concentration were monitored as a critical quality control parameter
852
during wine fermentation. The proposed method successfully predicted both parameters and
853
offered great prospect to be used for in-line monitoring of different fermentation parameters
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(Q. Wang, Li, Ma, & Si, 2015). A new method was developed for determination of phenolic
855
compounds in Cabernet Sauvignon red wines. The Raman scattering spectra was captured for
856
red wines corresponding to different vineyards, seasons, and harvests. The high correlation
857
was observed for polyphenols, tannins, and anthocyanins, indicating the potential of Raman
858
spectroscopy for field measurements (Gallego, Guesalaga, Bordeu, & González, 2010). The
859
novel technique was investigated for monitoring time lapse changes during wine
860
fermentation. The auto calibration system comprising of measurement optical path and
861
reference optical path was used to eliminate the intensity shift and frequency shift of Raman
862
spectra. The experimental results presented excellent prediction accuracy for ethanol,
863
glycerol, and sugar content using PCA-PLS model (Q. Wang, Li, Ma, & Liang, 2014). The
864
Raman spectroscopy was also successfully used to predict the polyphenol content and density
865
of apple liqueurs using PLS-1 model (Śliwińska, et al., 2016).
ro
of
854
The discrimination of Chinese rice wine at fermentation stages was performed using
867
Raman spectroscopy. The achieved results reported good performance with an average
868
correct classification rate of 94.9% based on DPLS build model (Wu, Xu, Long, Wang, et al.,
869
2015a). The Chinese rice wines belonging to different brands were also differentiated with
870
100% correct classification in prediction set using LDA model (Wu, Long, et al., 2016). In
871
another study, wine discrimination was carried out with respect to vintage, variety, and
872
geographical origin. The classification rate of 100% for both initial and cross-validation was
873
achieved for variety and geographical origin, while for vintage discrimination, 100% in initial
874
and 94.1% in cross-validation was reached (Magdas, Guyon, Feher, & Pinzaru, 2018). The
875
species of Leuconostoc, Oenococcus, Pediococcus, and Lactobacillus play an imperative role
876
in winemaking process, either as contaminants or inoculants. The metabolites of these lactic
877
acid bacteria greatly influence aroma, flavor and texture of wine. Thus, a robust model for
878
discrimination of lactic acid bacteria with a sensitivity ranging from 84% to 90% was
879
proposed using Raman spectral fingerprints (Rodriguez, Thornton, & Thornton, 2017). The
880
discrimination method was developed to identify counterfeit and adulterated samples in
881
single malt scotch whisky using Raman spectroscopy. The results explicated that PCA
882
discriminated mainly cask type, in which whisky was aged. Whereas, PLS regression enabled
883
the investigation of alcohol content, age, type of filtration process used and presence of
884
artificial color in whisky (Kiefer & Cromwell, 2017). The Raman spectral fingerprints were
885
also successfully used to distinguish silver tequila from aged tequilas (Frausto-Reyes,
886
Medina-Gutiérrez, Sato-Berrú, & Sahagún, 2005). Similarly, a fast method has been proposed
887
for discrimination among mezcal samples with variable aging time using Raman spectral data
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888
integrated with PCA and PLS-DA model (Elías, et al., 2015). In conclusion, Raman
889
spectroscopy offers great capability to solve problems related to quality control, safety
890
monitoring, discrimination and authentication of alcoholic beverages. [Table 3 here]
891 892
3.4. Fluorescence spectroscopy In recent decades, remarkable growth has been observed in the application of
894
fluorescence spectroscopy. Multiple components can be determined simultaneously within
895
few minutes with improved sensitivity and selectivity. Fluorescence spectroscopy as a
896
nondestructive, accurate, and reliable technique has offered great potential in quality control,
897
adulteration monitoring and geographical identification of alcoholic beverages. Previously,
898
brandies and mixed wine spirits have been successfully discriminated by mean of
899
synchronous fluorescence spectra tandem with backward LDA model (Uríčková, Sádecká, &
900
Májek, 2013). Another scientific opinion has followed the suite by differentiating whisky,
901
brandy, juniper and slivovice drinks using synchronous fluorescence spectra (Tóthová, 2008).
902
Similarly, front face fluorescence spectroscopy was explored to differentiate counterfeit
903
brandies from wine distillates. The synchronous application of fluorescence spectra (200-700
904
nm) and emission spectra (360-650 nm) resulted in good discrimination among the two spirit
905
classes through PCA and HCA. The higher rate of misclassification was observed using
906
excitation spectra between 225 to 425 nm at an emission wavelength of 440 nm (Sádecká,
907
Tóthová, & Májek, 2009). The front-face fluorescence spectroscopy was also exploited for
908
discrimination of wines according to typicality, variety, and vintage. The shape of emission
909
and excitation spectra changed with the wine samples. The excitation spectra were indicative
910
of good discrimination between German and French wines using PCA. Similarly, emission
911
fluorescence data set led to 95% correct classification of typical and non-typical Beaujolais
912
(Dufour, Letort, Laguet, Lebecque, & Serra, 2006). The modeling of emission-excitation
913
matrices to classify Argentine white wines according to the grape varieties has also been
914
carried out. The PCA score plot presented strong overlapping between different classes. The
915
results yielded U-PLS-DA and SPA-LDA as an efficient models for discrimination of white
916
wines with an accuracy of ca. 90% (Azcarate, et al., 2015). A simpler method for
917
discrimination of fruit spirits using synchronous fluorescence spectroscopy has been
918
employed previously. The general DA model led to 100% classification rate compared to that
919
of LDA model for fruit spirits (Tomková, Sádecká, & Hroboňová, 2015). Polyphenols
920
composition of wines is governed by the variety of grape used during the process. The
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921
fluorescence spectral data acquired at neutral pH are not always sufficient to differentiate the
922
wines. The pH modification results in structural changes in polyphenols leading to significant
923
alterations in their spectra, making it possible to more precisely differentiate wine samples.
924
Independent component analysis captured the informative signals from the matrix and
925
showed significant improvement in discrimination of wines with pH modification (Saad,
926
Bouveresse, Locquet, & Rutledge, 2016). The addition of ethanol or water is a most common mean to adulterate brandy. To avoid
928
misleading of consumers, excitation-emission matrix fluorescence coupled with parallel
929
factor analysis and PLS regression was used to quantify the concentration of methanol,
930
ethanol, and water in adulterated brandy. The developed models were able to predict studied
931
parameters with coefficients of determination greater than 0.993, and RMSEP ranging from
932
0.20% to 0.24% (Markechová, Májek, Kleinová, & Sádecká, 2014). In another study, brandy
933
adulteration with mixed wine spirit was determined by mean of fluorescence spectroscopy.
934
The model created using parallel factor analysis was capable of predicting different levels of
935
mixed wine spirit in adulterated brandy with higher correlation coefficient (0.995) and lower
936
RMSEP (1.9%) (Markechová, Májek, & Sádecká, 2014). Similarly, fluorescence
937
spectroscopy was used to determine gallic, vanillic, syringic and ferulic acids along with
938
scopoletin in brandy using PLS2 model. The developed method was able to predict chemical
939
compounds with higher correlation coefficient and lower SEP values. The reported results
940
were comparable to those acquired by means of HPLC reference method (Žiak, Sádecká,
941
Májek, & Hroboňová, 2014). Evidently, these tools could be helpful for discrimination,
942
quality control, and adulteration assessment of alcoholic beverages in mass production units.
943
3.5. Nuclear magnetic resonance spectroscopy
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The NMR spectroscopy is becoming more and more popular for testing of alcoholic
945
beverages. A single NMR measurement can answer numerous quality related issues that
946
could not be resolved through traditional methods. A number of scientific opinions have
947
highlighted the use of NMR technique for identification, quality control, and adulteration
948
detection of alcoholic beverages. Previously, characterization of wines produced from grape
949
variety “Greco bianco”, in different Italian regions, i.e. Campania and Calabria were achieved
950
by means of NMR and conventional analysis. The results achieved significant discrimination
951
using PCA and PLD-DA method. In particular, PLD-DA allowed the selection of compounds
952
(glucose, proline/arginine ratio, total acidity, and total polyphenol index) that were useful for
953
discrimination of wines based on their geographical origin (Caruso, et al., 2012). In a study
954
involving beers, NMR spectral data tandem with PCA demonstrated promising potential in 28
detection of compositional differences attributed to time and site differences (Almeida, et al.,
956
2006). Similarly, NMR spectroscopy was employed to differentiate Brazilian lager beers of
957
two classes, varying in style and information available on the label (da Silva, Flumignan,
958
Tininis, Pezza, & Pezza, 2019). The acquired results reveled higher rate of discrimination
959
through SIMCA and PLS-DA model. In another scientific opinion, NMR spectroscopy
960
showed that lagers and ales beers differ primarily in their aromatic compositions, thus can be
961
discriminated on a PCA scores plot (I. Duarte, et al., 2002). NMR spectroscopy was also
962
explored for quality control of beers differing in label and types. In particular, most lagers,
963
ales and alcohol-free sample were discriminated by means of their aromatic compositions,
964
reflecting higher sensitivity of NMR regions towards different fermentation processes (I. F.
965
Duarte, Barros, Almeida, Spraul, & Gil, 2004). NMR spectral profiles have been fused with
966
stable isotope (SNIF-NMR,
967
fusion model yielded improvement up to 100%, compared to that of single NMR data (82-
968
85%) for prediction of geographical origin. Though some improvement was observed for
969
vintage year (99% for fusion model), however, detection of varietal differences of grape
970
stayed stagnant (Monakhova, et al., 2014). In another study, genotoxic carcinogen (ethyl
971
carbamate) was analyzed by mean of NMR spectroscopy in stone fruit spirits. The results
972
presented reliable quantification of ethyl carbamate below the target level (1 mg/L)
973
recommended by European Commission (Monakhova, Kuballa, & Lachenmeier, 2012).
974
Similarly, NMR spectroscopy was explored for authenticity and quality control of beers.
975
NMR spectra correlated with wet chemical analysis and offered great capability to predict
976
lactic acid, ethanol and gravity. The significant discrimination was observed for beers with
977
deterioration in quality. The beer made from wheat malt could also be differentiated from
978
those of barley malt. The clustering of beers from the same brewing sites was also noticed
979
(Lachenmeier, et al., 2005). These observations highlight the importance of NMR
980
spectroscopy as a powerful tool in quality control, identification of geographical origin and
981
authenticity assessment of alcoholic beverages.
982
3.6. Electronic tongue
13
ro
O,
O) to enhance the classification accuracy of wines. The
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983
The e-tongue sensor array integrates the concept of global selectivity to distinguish very
984
similar liquids, where the changes in electrical response of various materials act as a
985
fingerprint for the investigated sample. The captured fingerprints are mathematically
986
processed to make them meaningful. The applications of e-tongue system for quantitation,
987
discrimination, and authentication of alcoholic beverages has been summarized in Table 4.
988
The parameters related to phenolic compounds were investigated using e-tongue system in 29
red wines. The eight type of Spanish wines varying in age, grape varieties and geographical
990
regions were analyzed. Th PLS regression was used to develop prediction models with higher
991
correlation (Rp2 >0.8944) and lower error for prediction (RMSEp<2.60) for parameters related
992
to phenolic content of red wines (Garcia-Hernandez, et al., 2020). The potentiometric e-
993
tongue technique was also successfully used to quantify ferulic acid, sinapic acid and gallic
994
acid with correlation coefficient higher than 0.976 in beers (Cetó & del Valle, 2014). The
995
training model for alcohol content in beer was developed with r and RMSE of 0.995 and
996
0.21%, respectively (Cetó, Gutiérrez-Capitán, Calvo, & del Valle, 2013). The portable e-
997
tongue system based on an array of electrochemical screen-printed electrodes was explored
998
for investigation of alcohol strength and color index. The developed multivariate model was
999
able to predict alcohol strength and color index with an accuracy of 86% and 76%,
1000
respectively (Blanco, De la Fuente, Caballero, & Rodríguez-Méndez, 2015). Apart, the
1001
voltammetric e-tongue system was used to predict the total sugars and dry extract of cava
1002
wines with different ageing time. The quantitative models employing ANNs revealed higher
1003
accuracy and precision for investigated parameters (Cetó, Capdevila, Puig‐Pujol, & del
1004
Valle, 2014). The prediction model for different marked ages was established based on
1005
voltammetric e-tongue system. The regression results presented higher correlation for
1006
Chinese rice wine with different marked ages using PLS and BP-ANN model (Zhenbo Wei,
1007
Wang, & Ye, 2011). The taste sensing system was employed to predict the flavor and marked
1008
age in rice wine. The SVM built model relying on leave-one-out cross validation showed
1009
more accuracy and stability than that of PLS regression model with prediction correlations of
1010
0.9620, and 0.9568 for flavor and marked age, respectively (ZhenBo Wei, Wang, Cui, &
1011
Wang, 2016). The ELM built model successfully predicted the geographical origin of rice
1012
wines with R2, and RMSEC of 0.9436, and 0.36 in testing set, respectively (J. Wang, Zhu,
1013
Zhang, & Wei, 2019). The aging time and global sensory score of wine was also predicted
1014
with e-tongue system. The PLS model yielded r values of 0.969, and 0.917, with NRMSE
1015
0.077, and 0.110 in the prediction set, for aging time and sensory scores, respectively (Cetó,
1016
et al., 2017). The hybrid e-tongue based on voltammetric and potentiometric sensors was
1017
further assessed for process duration, ethyl alcohol content, density, extract content, and
1018
reducing sugars (Kutyła‐Olesiuk, Wesoły, & Wróblewski, 2018). The results indicated that
1019
hybrid system outperformed with excellent correlation coefficient for quantitative assessment
1020
of wine production. In another study, the capability of voltammetric e-tongue to analyze the
1021
phenolic content and density of apple liquors (Nalewka) was compared with spectroscopy
1022
techniques (Raman, and UV-Vis spectroscopy). The PLS-1 model based on e-tongue system
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yielded better or nearly similar correlation for aforementioned parameters in calibration and
1024
validation sets, compared to that of spectroscopy technique (Śliwińska, et al., 2016).
1025
Similarly, E-tongue technique based on 18 potentiometric chemical sensors was applied for
1026
quantitative investigation of beer. A good correlation was observed between sensors and
1027
physicochemical variables (bitterness, polyphenols, alcohol volume, and real extract) with
1028
RMSEP in the range of 0.60 Plato˚ to 52.00 mg/L (Polshin, et al., 2010). The sugars, organic
1029
acids, amino acids, and sensory score were predicted in Korean rice wines using e-tongue
1030
system. The developed system well predicted all the sensory attributes except bitterness with
1031
acceptable r2. The precision and accuracy of built models were lower for chemical
1032
compounds responsible for taste attributes, except for tryptophan, succinate, lactate and
1033
ribose (Kang, Lee, & Park, 2014). The voltammetric Bio-e-tongue system was successfully
1034
investigated for prediction of phenolic compounds in rosé cava wines. The acquired results
1035
revealed excellent prediction with r, and RMSE in the range of 0.755 to 0.896, and 0.74 arb.
1036
unit to 41.4 mg L-1, respectively (Cetó, Capdevila, Mínguez, & del Valle, 2014).
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The red wines samples were discriminated using PCA analysis coupled with e-tongue
1038
technique. The analysis could classify wines based on their phenolic contents with PC1, PC2
1039
and PC3 explaining 85.8% of the total variance between red wine samples (Garcia-
1040
Hernandez, et al., 2020). The fermentation time (0, 2, 4, 6, 8, 10, 18, & 26 days) of
1041
pomegranate wine was also successfully discriminated using PCA (Lan, et al., 2017). The
1042
beer sample of different style was discriminated using LDA methods. The pruning step and
1043
windowed slicing integral method were used as a data reduction strategy. The correct
1044
classification rate of different beer styles was recorded at 81.9% for individual sample (Cetó
1045
& del Valle, 2014). The discrimination model for different commercial beers was developed
1046
using e-tongue system based on potentiometric ion-selective electrodes. The samples were
1047
analyzed without any pretreatment. The stepwise inclusion method based on Mahala Nobis
1048
distance criteria for selection of variables was used to capture the informative variables for
1049
discrimination. The LDA built model based on selected variables proved to be good
1050
alternative for the correct classification under complex situation (Cetó, et al., 2013). The
1051
LDA classification model established to identify different types of beers was able to
1052
discriminate samples with 100% accuracy (Blanco, et al., 2015). The classification of cava
1053
wines with variable ageing time was explored by employing voltammetric e-tongue. The
1054
acquired results achieved higher discrimination of cava wines according to the vintage time
1055
(Cetó, Capdevila, Puig‐Pujol, et al., 2014). The Chinese rice wines of different marked ages
1056
(1, 3 and 5) were also discriminated by PCA and CA, using voltammetric e-tongue system.
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31
The PCA scatter plot revealed clear discrimination for configuration of each sample with a
1058
total variance of 96.92% (Zhenbo Wei, et al., 2011). The taste sensing system was explored
1059
to discriminate three type of Chinese rice wines. Better results were achieved with DFA in
1060
classification of rice wines with different marked ages, whereas, PCA and DFA were similar
1061
in discrimination of wines with different flavors (ZhenBo Wei, et al., 2016). The
1062
voltammetric e-tongue system was further explored for identification of rice wines of
1063
different geographical origin. The nanocomposites modified electrode exhibited higher
1064
sensitivity to gallic acid, tyrosine, and guanosine-5-monophosphate disodium salt, having
1065
good correlation with geographical origin of wines. The area method was used to extract the
1066
informative variables from original data. The locality preserving projection presented better
1067
results for classification of geographical origin compared to that of LDA model (J. Wang, et
1068
al., 2019). The e-tongue system was also used to discriminate the red wine based on degree of
1069
maturation in barrels. The satisfactory results were obtained with LDA model accounting
1070
100% classification for different types of barrels used in wine maturation and ageing (Cetó, et
1071
al., 2017). A hybrid e-tongue system was explored for qualitative assessment of wine making
1072
process. The PCA and PLS-DA built models showed higher percentage of classification,
1073
confirming the reliability of hybrid e-tongue system for assessment of wine making (Kutyła‐
1074
Olesiuk, et al., 2018). In another study, voltammetric e-tongue system demonstrated better
1075
discrimination capability between apple liqueurs prepared from different apple varieties,
1076
when compared to that of Raman and UV-Vis spectroscopy (Śliwińska, et al., 2016). The
1077
Chinese rice wines sourced from different vintage year (2003, 2005, 2008, & 2010) were
1078
successfully discriminated based on taste-active compounds and the sensory attributes (Yu,
1079
Zhao, Li, Tian, & Ma, 2015). The portable multi-electrode e-tongue system was explored for
1080
discrimination of rice wines according to four different marked ages (3, 5, 8 and 10 years).
1081
Compared to other models, BPANN yielded better results with 100% success in both
1082
calibration and prediction sets (Ouyang, Zhao, & Chen, 2013). In another scientific opinion,
1083
e-tongue system based on voltammetric and potentiometric sensor array was used for
1084
identification of different style of beer. The information from both the sensors was
1085
preprocessed and combined to improve the classification ability of model. The results based
1086
on information obtained from both sensors were able to differentiate 100% of different style
1087
(lager, stout, & IPA) of beers as well as its production process (J. Gutiérrez, Moreno-Barón,
1088
Cetó, Mimendia, & del Valle, 2012). The different brands of Chinese rice wine were
1089
discriminated using ANN model with an average accuracy of more than 90% (Lu & Cai,
1090
2014). A method was developed for brand uniformity control of monoculture Apulian red
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32
wines (Primitivo and Negroamaro). The PLS-DA applied to output data permitted a correct
1092
discrimination of more than 70% in respect to original brand affiliation (Lvova, et al., 2018).
1093
The two different types of wines (white, and red) were discriminated using PCA and ANN.
1094
The proposed method was able to identify both categories yielding 98% success rate with
1095
ANN model (Díaz & Acevedo, 2014). In another study, e-tongue system coupled with LDA
1096
model was able to discriminate cava wines with 80-96% correct classification (Giménez-
1097
Gómez, et al., 2016). A clearer discrimination between dark and pale lager beer was observed
1098
using PCA (Arrieta, Rodríguez-Méndez, De Saja, Blanco, & Nimubona, 2010). Another low-
1099
cost method was proposed to discriminate whisky and wines using disposable voltammetric
1100
e-tongue system, fabricated with copper and gold substrates. The cheap and expensive
1101
whisky samples were successfully discriminated using only copper electrode. Likewise, e-
1102
tongue system based on both electrodes was able to discriminate different brands of wine and
1103
identify differences in the type of wine i.e. soft white, dry white, soft red, dry red
1104
(Novakowski, Bertotti, & Paixão, 2011). These studies may provide a valuable guideline for
1105
improving quality control, functional feature, discrimination, and authentication of alcoholic
1106
beverages through e-tongue.
lP
[Table 4 here]
3.7. Electronic nose
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Flavor is a complex phenomenon, different chemical compounds and sensory modalities
1110
influence the perception of flavor. The aroma or smell is largely attributed to the volatile
1111
compounds, considered to be major contributor to overall perception of flavor in alcoholic
1112
beverages (Polášková, et al., 2008). The progress on artificial olfactory instruments
1113
mimicking the biological system started with the discovery of materials having chemo-
1114
electronic properties (Röck, et al., 2008). Over past two decades, large number of scientific
1115
studies have been devoted towards the advancement of e-nose (Table 5). The aroma volatiles
1116
of Apulian wines made by autochthonous grape varieties (Primitivo and Negroamaro) were
1117
extracted by solid phase extraction (SPE) and investigated via GC-MS in combination with e-
1118
nose. The sensor data was analyzed, and good discrimination was achieved using PCA. The
1119
VOCs determined by GC-MS were predicted using PLS and RSR analysis. Satisfactory
1120
results were achieved using RSR technique for 18 wine odorant concentrations compared to
1121
PLS (Capone, et al., 2013). The spoilage induced by Brettanomyces yeast was also analyzed
1122
using MS e-nose instrument in two commercial red wine varieties in Australia (Shiraz, and
1123
Cabernet Sauvignon). The data analyzed through SLDA correctly classified 67% of the red
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33
wine samples in three different categories namely low (lower than 100 µg L-1), medium
1125
(between 500 to 200 µg L-1), and high (higher than 500 µg L-1), in respect to yeast spoilage.
1126
The concentration of 4-ethylphenol was also predicted using PLS built model with R2cal of
1127
0.70, and 0.90 for red wine sample in barrel and bottle, respectively (Cynkar, et al., 2007). A
1128
portable e-nose was used to classify rice wines with different marked ages (3, 5, 10, 15, and
1129
20 years). The LDA model based on feature extraction data provided the best discrimination
1130
results for different marked ages of rice wines. The marked ages were also successfully
1131
predicted with R2 0.9942, using SVM model (Zhebo Wei, Xiao, Wang, & Wang, 2017). A
1132
comparative study of metal oxide and mass spectrometry-based e-nose system was performed
1133
to predict two components of taints (4-ethylphenol, 4-ethylguaiacol), which are metabolites
1134
produced by Brettanomyces yeasts. The results reported that e-nose system based on metal
1135
oxide sensor was unable to classify “brett” spoilage owing to lack of gas sensor’s response to
1136
inter sample variation in volatile compounds other than ethylphenols. Contrarily, mass
1137
spectrometry-based e-nose system successfully predicted the concentrations of both
1138
components with high correlation coefficient (Berna, Trowell, Cynkar, & Cozzolino, 2008).
1139
In another study, better prediction results were obtained for sensory attributes and chemical
1140
components (Jesus Lozano, et al., 2007). A quantitative method was proposed for prediction
1141
of ageing time of sugar cane spirits in oak barrels using headspace-mass spectrometry e-nose
1142
system. The method was relatively simple and produced results for aging time with an
1143
accuracy of ≈ one month (Martí, Pino, Boqué, Busto, & Guasch, 2005). The Chinese
1144
premium liquors were successfully differentiated for their peculiar flavor style and origin
1145
using e-nose system combined with HCA and PCA. The 30 different volatile compounds
1146
were identified, which showed good synchronization with liquor characteristics (Xiao, et al.,
1147
2014). The potential of GC based e-nose system was explored for discrimination of rice wine
1148
age (1, 3, and 5 years). The results led to 96% correct classification using DA model (Yu,
1149
Dai, Yao, & Xiao, 2014). The flavor assessment of Chinese spirits was also carried out using
1150
e-nose system. The CA and PCA confirmed that S7 (sulphur-organic) and S9 (aromatics
1151
compounds, sulphur-organic) sensors could recognize Fougere-flavor and Feng-flavor, S1
1152
(aromatic compounds, toluene), S3 (aromatic compounds, ammonia), and S5 (alkenes,
1153
aromatic compounds), could identify Zhima, Mild, Strong, Jiang, and Nong jiang-flavor,
1154
while, S6 (broad-methane) could separate Laobaigan-flavor. The results reported that SVM
1155
built model was capable of differentiating Nong jiang, Zhima, Mild, Strong, and Jiang-flavor
1156
(M. Liu, et al., 2012). The metal oxide semiconductor-based e-nose has offered great
1157
capability for age fingerprinting of alcoholic beers with classification accuracy of 90%
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1124
34
(Mahdi Ghasemi-Varnamkhasti, et al., 2011). The four types of Barbera wines procured from
1159
different geographical origin were classified using PCA, LDA, and CART analysis. During
1160
classification, best results were obtained from LDA, yielding 98.1% correct prediction and
1161
100% correct assignation (Buratti, Benedetti, Scampicchio, & Pangerod, 2004). Similarly,
1162
four types of Madrid wines were successfully identified using PCA and PNN model with
1163
excellent overall classification success (Santos, et al., 2004). The chaotic neural network
1164
entitled KIII was applied to e-nose technique for classification of six typical VOCs in
1165
Chinese rice wines. In comparison with BP-ANN, results indicated KIII network exhibited
1166
better performance in classification of VOCs with variable concentrations (Fu, Li, Qin, &
1167
Freeman, 2007). A nanostructure sensor based on gold doped zinc oxide was fabricated and
1168
used as a portable e-nose for discrimination of VOCs in red and white wines. The fabricated
1169
sensor was sensitive to VOCs, especially ethanol vapors. As per results, portable e-nose
1170
offered great capability to detect difference between alcoholic solutions and alcoholic
1171
beverages and differentiate red and white wines having same alcohol percentage
1172
(Wongchoosuk, Choopun, Tuantranont, & Kerdcharoen, 2009). The e-nose system in
1173
combination with HS-MS was even used to characterize and classify series of beers
1174
according to their chemical composition and production sites. From the results, it was visible
1175
that HS-MS e-nose offered great potential not only to differentiate beer samples but also
1176
delivered information about the specific compounds accountable for discrimination (Vera, et
1177
al., 2011). Likewise, the Tempranillo wines were successfully classified with good accuracy
1178
according to their geographical origin (Australia and Spain) using SLDA in combination with
1179
MS based e-nose system (Cynkar, Dambergs, Smith, & Cozzolino, 2010). The aroma
1180
characterization of alcoholic drinks is seemingly a difficult task owing to masking effect of
1181
ethanol. The e-nose system based on tin dioxide multisensor array was used to recognize 29
1182
different aroma compounds in white wine. The results indicated that in spite of great
1183
influence of ethanol and other wine compounds, the build system correctly classified added
1184
aromatic compounds to detect the adulteration with 97.2% of accuracy (J Lozano, Santos, &
1185
Horrillo, 2005). Likewise, off-flavor characterization of wines was successfully performed
1186
using dehydration and desalcoholization step through PCA method even at concentrations
1187
corresponding to perception threshold of human expert (Ragazzo-Sanchez, Chalier, &
1188
Ghommidh, 2005). In another scientific opinion, successful discrimination was carried out for
1189
different wines of the same grape variety using PCA coupled e-nose system based on surface
1190
acoustic wave devices (García, et al., 2006). The differentiation between fermented and non-
1191
fermented musts was also achieved in sweat wines using PCA method (García-Martínez, et
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1158
35
al., 2011). The e-nose system offered greater perspectives to accurately differentiate between
1193
Cabernet Sauvignon wines produced from grapes that have received different pre-harvest and
1194
post bloom spray treatment to enhance their growth (Martin, Mallikarjunan, & Zoecklein,
1195
2008). The discrimination of two fungal species (Aspergillus niger, and Aspergillus
1196
carbonarius) responsible for contamination of wines were performed through volatile
1197
compounds they produce. The e-nose system based on metal oxide sensors coupled with PCA
1198
method was capable to discriminate between the two species within 48 h of growth on yeast
1199
extract sucrose and white grape juice agar, accounting more than 97% of the data. The
1200
differentiation was also confirmed by CA method (Cabañes, Sahgal, Bragulat, & Magan,
1201
2009). To control fraudulent practices, novel method was developed for detection and
1202
recognition of wine ageing. Purposely, two different measurement were performed; firstly,
1203
same wine was aged in different oak barrel (American and French oak), secondly wines
1204
procured from different wine cellar made with same grape variety aged in American and
1205
French oak. A classification success of 97% was recorded with ANNs method in detection of
1206
different ageing processes for the tested wines (J Lozano, et al., 2008). Previously, good
1207
discrimination for fermentation time of pomegranate wines was recorded with the use of PCA
1208
(Lan, et al., 2017). The PCA method also exhibited good discrimination for Spanish wines
1209
obtained form same viticulture zone and grape variety (Bellincontro, García-Martínez,
1210
Mencarelli, & Moreno, 2013). The simple classification through machine learning methods
1211
was evaluated for discrimination of beer using e-nose data. The acquired result for built
1212
models (LDA, SIMCA, PLS-DA, SVM) reported 100% classification accuracy for both
1213
training and test sets (Ghasemi-Varnamkhasti, Mohtasebi, Siadat, Ahmadi, & Razavi, 2015).
1214
However, a scientific study has also reported higher rate of misclassification for
1215
discrimination of geographical origin using metal oxide based e-nose (Berna, Trowell,
1216
Clifford, Cynkar, & Cozzolino, 2009). Conversely, clear visualization was observed among
1217
different classes of beer, wines, and spirit using PCA, and DFA methods (Ragazzo-Sanchez,
1218
Chalier, Chevalier, Calderon-Santoyo, & Ghommidh, 2008). An e-nose system using
1219
headspace as an extraction technique was used for identification and classification of
1220
aromatic compounds in red and white wines. The PCA showed clear separation, whereas,
1221
ANN revealed more than 98% success rate in classification of both wines (Jesús Lozano, et
1222
al., 2004). In another study, alcoholic beverages (wines, and beer) tainted with off-flavor
1223
were assessed through e-nose. The desalcoholization and dehydration procedure were
1224
performed before analysis. The off-flavor detection was successful in wines and beer samples
1225
using PCA and DFA method. Identification of differences among given beer and tainted beer
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1192
36
of the two different brands was difficult than that of same brands (Ragazzo-Sanchez, et al.,
1227
2009). The portable e-nose system combined with hybrid carbon nanotube-SnO2 gas sensors
1228
was fabricated for detection of methanol contamination in whiskeys. The developed
1229
instrument employed feature extraction procedure including primary and integral derivatives,
1230
which lead to greater discrimination accuracy compared to that of classical feature (ΔR, and
1231
ΔR/R0). The proposed system offered great capability to monitor and classify pure and
1232
methanol contaminated samples (Wongchoosuk, Wisitsoraat, Tuantranont, & Kerdcharoen,
1233
2010). Similarly, two sensor arrays were developed for e-nose system, one with platinum and
1234
the other one with polysilicon integrated heater to monitor wine quality. The sensor array
1235
with polysilicon integrated heater reported 100% classification success using PNN for
1236
different kinds of wine (Aleixandre, et al., 2008). Similar results were also reported with
1237
correct classification rate of more than 90% of wines by their varietal origin using DPLS
1238
model (Cozzolino, Smyth, Cynkar, Dambergs, & Gishen, 2005). The effect of different
1239
temperatures on the headspace of wines was explored using MS based e-nose system. The
1240
fingerprints of volatiles captured from headspace were more affected in white wine, while red
1241
wine volatiles were seemingly less affected. This practical application demonstrated that care
1242
should be exercised in the selection of optimal temperature when recording fingerprints of
1243
wine using headspace (Cozzolino, Cynkar, Dambergs, & Smith, 2010). A comparation was
1244
made between human sensory panel and e-nose system for perception and recognition of
1245
thresholds of volatile components. The results revealed that the perception level of e-nose
1246
was lower in relation to human nose, albeit e-nose presented better output in recognition
1247
threshold of some aroma, offering great perspectives to be used as complementary tool to
1248
human sensory panels (Arroyo, et al., 2009). Conclusively, e-nose system based on different
1249
sensor array could be used for identification of origin, contamination detection, and
1250
authenticity assessment of alcoholic beverages at an industrial scale with full confidence.
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[Table 5 here]
1251 1252
3.8. Colorimetric sensor array
1253
The specialized brewing techniques and different raw material produce a variety of
1254
flavors, indicative of quality or grade of alcoholic beverage. The average price of alcoholic
1255
beverages might vary from few to thousands of dollars depending upon geographical origin,
1256
brand, and flavor. Thus, counterfeiting and adulteration of renowned brands to obtain higher
1257
profit has frustrated alcoholic beverage industry. In addition, increasing concerns have arisen
1258
about protection of geographical integrity as a part of agriculture policy in both European 37
1259
countries and P. R. of China. Therefore, reliable and efficient discrimination of alcoholic
1260
beverages carry an important cultural and economic value. Previously, colorimetric sensor array was fabricated for discrimination of commercial and
1262
high alcohol Chinese base liquors of different brands and flavors. The four chemical groups
1263
of volatile compounds were analyzed by GC-MS to serve as markers in base liquors. The
1264
developed calorimetric sensor array captured sensitive interactions with volatile markers. The
1265
results enabled identification of either commercial liquors of same flavor or base liquors with
1266
high-alcohol volume through color changes. The response of the sensor was greatly
1267
improved, and no misclassification was observed for both liquors (J.-J. Li, et al., 2014).
1268
Similarly, commercial beers were investigated in gas and liquid phases using colorimetric
1269
sensor array. The results yielded facile identification of beers in either liquid or gas phase
1270
through comparison of the color changes by means of HCA statistical analysis (Zhang,
1271
Bailey, & Suslick, 2006). In another study, colorimetric sensor array was used to determine
1272
astringency, sourness, and sweetness of white wines by mean of PCA and ANN model. The
1273
PCA reduced color variables to three PCs and differentiated between less sweet and sweet
1274
sets of wines. The ANN model quantitatively predicted the astringency, sourness, and
1275
sweetness with higher correlation coefficient. The colorimetric sensor array of higher
1276
potential used in industries for identification of taste components can provide the level of
1277
astringency, sourness, and sweetness to the consumers (Chung, et al., 2015). The colorimetric
1278
artificial nose was also used to identify and characterize Chinese liquors procured from
1279
different geographical regions. Data indicated that each category of liquor could be grouped
1280
together using PCA and HCA plot. The LDA model reported 100% correct classification for
1281
different categories of Chinese liquors (Qin, et al., 2012). Likewise other study also reported
1282
classification accuracy of 100% for identification of different baijiu styles by mean of LDA
1283
model (Ya, et al., 2012). The authors reported discrimination of commercial baijiu using
1284
fabricated colorimetric sensor array. From the analysis, eight types of baijiu samples were
1285
distinguished significantly by mean of PCA, LDA and HCA without misclassification
1286
(Zheng, et al., 2018). This summary about colorimetric sensor array could be helpful for
1287
quality control of alcoholic beverages in the market and mass production factories.
1288
3.9. Combination of Sensors
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1289
The utilization and advancement in different sensors have made available more and more
1290
data for integration of two or more sensor fusion for innovative results. The fusion of data
1291
from different sensors are said to be analogous to the cognitive process, that is utilized by
1292
human to continually combine the data from different senses to make inferences about 38
surrounding environment (Xiaobo & Jiewen, 2005). The processing of integrated data
1294
acquired from different techniques or sensors is known as data fusion (Hall & Llinas, 1997).
1295
The literature survey related to combination of sensors for quantitation, discrimination, and
1296
authentication of alcoholic beverages has been presented in Table 6. Previously, application
1297
of ATR-IR, Raman spectroscopy and concatenated ATR-IR and Raman spectroscopy were
1298
investigated for determination of total phenolic content and antioxidant capacity of Chinese
1299
rice wine. The combinations of SiPLS, SVM and PCA were applied to process the combined
1300
data from these nondestructive instrumental techniques. The SVM models based on efficient
1301
spectral intervals selected by SiPLS from ATR-IR and Raman spectra were superior to PLS
1302
models based on the same captured variables, and PLS models based on ATR-IR or Raman
1303
spectra (Wu, Xu, et al., 2016). The combination of NIR and MIR spectroscopy was employed
1304
for quantitation of real extract, original extract, and alcohol content in beer. Slightly better
1305
prediction performance was recorded with combination of NIR and MIR spectral data using
1306
ANN model for all the parameters. The RMSEP for determination of ethanol, real extract and
1307
original extract ranged from 0.076% v/v to 0.14% v/v (Iñón, Garrigues, & de la Guardia,
1308
2006). The integration of e-nose and e-tongue sensors was used to predict sensorial
1309
descriptors of Italian red dry wines. The results reported better prediction of large part of the
1310
selected sensorial parameters except sourness by mean of genetic algorithms (Buratti, et al.,
1311
2007). Likewise, combination of e-nose and e-tongue sensors based on arrays of
1312
metalloporphyrin was explored to predict sensory descriptors and 23 chemical parameters.
1313
The combination of two arrays enhanced the prediction performance of build models for both
1314
quantitative and qualitative parameters (Di Natale, et al., 2004). In another scientific opinion,
1315
an electronic panel (e-nose, and e-tongue) was explored to analyze oxygen related
1316
parameters, polyphenols and color indices in red wines. The obtained results yielded higher
1317
correlation coefficient for calibration and prediction sets with RMSEP ranging from 0.193
1318
mg L-1 to 166.37 mg L-1 for all the parameters using PLS1 model (Rodriguez-Mendez, et al.,
1319
2014).
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1320
Likewise, an electronic panel based on e-tongue and e-nose sensors was explored for
1321
prediction of different classes of wines. The prediction and validation models developed
1322
using electronic panel indicated better capability of system to discriminate wines according to
1323
the use of micro-oxygenation by mean of PLS-DA model (Rodriguez-Mendez, et al., 2014).
1324
The combination of e-nose and e-tongue based on two arrays of metalloporphyrin was also
1325
successfully used to discriminate different wine classes by mean of PLS-DA model (Di
1326
Natale, et al., 2004). The combination of data acquired using e-tongue, e-nose and e-eyes was 39
used to investigate the aging of red wine. The electronic panel test in combination with PLS-
1328
DA model outperformed in discriminating wines when compared to traditional chemical
1329
analysis. Successful discrimination after ten months of aging was also observed between
1330
wines soaked with oak chips and the ones aged in French oak barrel of the same toasting
1331
level and origin, and treated with micropigmentation (Apetrei, et al., 2012). The fusion of
1332
three sensory tools; an array of electrochemical liquid sensors, an array of gas sensors, and an
1333
optical system was evaluated for discrimination of wines. The discrimination ability of
1334
system was greatly improved when data from each sensor was fused to form a multimodal
1335
feature vector (Rodríguez-Méndez, et al., 2004). The accumulated data from e-nose and e-
1336
tongue was used for classification of beer flavor. The PCA, GA-PLS, and variable
1337
importance of projection scores were applied to capture important information from the
1338
original fusion set to build robust models. The results showed that best classification was
1339
observed with ELM model built on selected feature variables (Men, et al., 2017). The hybrid
1340
e-tongue system based on data fusion of two sensor families was explored for discrimination
1341
of different beer types. The sensor arrays comprised of 15 potentiometric sensors and 6
1342
modified graphite-epoxy voltammetric sensors. The acquired results yielded successful
1343
qualitative classification by exploiting the new approach of data integration from two
1344
different sensors (J. M. Gutiérrez, et al., 2013). In another study, combination of UV, Vis,
1345
NIR and MIR spectroscopy was investigated to distinguish commercial Sauvignon Blanc
1346
wines from New Zealand, and Australia. The reported results showed better discrimination of
1347
geographical origin by the PLS-DA model based on concatenated MIR and NIR
1348
spectroscopy. The samples procured form Adelaide Hills of South Australia were
1349
misclassified (Cozzolino, et al., 2011a). Similarly, four types of barbera wines produced in
1350
northern Italy in an enclosed geographical areas were successfully classified by mean of LDA
1351
built model based on e-nose and amperometric e-tongue data (Buratti, et al., 2004). A clear
1352
discrimination of wine deterioration as a function of time was observed using sensor fusion of
1353
e-nose and e-tongue (Gil-Sánchez, et al., 2011). In conclusion, multisensor fusion approach
1354
offers good perspectives to be used as rapid tool to identify geographical region, quality
1355
control analysis, and safety assessment of alcoholic beverages.
1356 1357
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[Table 6 here] 4. Technical challenges and future perspectives
1358
During last two decades, nondestructive instrumental techniques or sensors have offered
1359
endless possibilities to investigate and predict changes related to quality control, adulteration 40
1360
assessment, geographical discrimination, and flavor profile of alcoholic beverages. The
1361
qualitative knowledge about key components of alcoholic beverages is very important but not
1362
enough to predict organoleptic and technological properties of individual brands or labels.
1363
The development of chemometrics coupled with nondestructive instrumental techniques or
1364
fabricated sensors allows the use of these methods as a rapid tool for in-line monitoring of
1365
quality parameters of alcoholic beverages. The cost saving, time reduction, environment
1366
friendliness, and potential saving nature of nondestructive instrumental techniques had
1367
brought them in limelight for analysis of alcoholic beverages. There is no doubt about the tremendous potential of nondestructive instrumental
1369
techniques, however, specific mathematical knowledge is required for development of robust
1370
models for the analysis of alcoholic beverages. Moreover, owing to different scale of signal,
1371
collinearity among variables, possible baseline shift, and instrumental drift, the chemometric
1372
analysis require extensive data pretreatment. In chemometrics, the manipulating approach is
1373
also used to enhance the prediction precision and model accuracy for simultaneous
1374
applications. In addition, high cost of the technologies and scientific developments attained at
1375
laboratory level are the major hurdles in their adaptation at industrial level. Accordingly,
1376
alcoholic beverage industry can practically seize the opportunity to implement these
1377
nondestructive instrumental techniques without requiring extensive and inefficient chemical
1378
analysis, thus improving the quality of drinks.
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Currently, the interest in producing alcohol free or low-alcoholic drinks from the
1380
alcoholic beverages is the biggest challenge for food processers owing to the growing health
1381
concerns associated
1382
dealcoholization methodologies such as distillation or evaporation efficiently remove ethanol,
1383
however, some organoleptic compounds may also be lost during the process, affecting the
1384
quality of the beverage (Castro-Muñoz, Galiano, & Figoli, 2019). Purposely, membrane-
1385
based technologies should be promoted in the alcoholic beverage industry to remove ethanol
1386
while retaining the quality of the alcoholic beverage (Castro-Muñoz, 2019a). Pervaporation is
1387
one such technique used not only for dealcoholization of alcoholic beverages but also for
1388
aroma recovery, thus meeting the quality parameters of non-alcoholic or low-alcoholic
1389
beverages (Castro-Muñoz, 2019b).
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with the consumption of alcohol. Albeit, post-fermentation
1390
Moreover, other membrane-based technologies such as ultrafiltration, microfiltration,
1391
membrane distillation, and nanofiltration could be explored to improve the quality of
1392
alcoholic beverages. Various applications of these innovative techniques have been
1393
developed such as extraction or purification of high value biomolecules from agro-industrial 41
1394
byproducts, recovery of aromatic compounds from processed and natural products, treatment
1395
of natural extracts, and production of non-alcoholic beverages being the popular ones
1396
(Castro-Muñoz, Boczkaj, Gontarek, Cassano, & Fíla, 2020; Castro-Muñoz, Díaz-Montes,
1397
Cassano, & Gontarek, 2020; Díaz-Montes, Gutiérrez-Macías, Orozco-Álvarez, & Castro-
1398
Muñoz, 2020). Additionally, one of the few limitations of nondestructive instrumental techniques is their
1400
inability to quantify lower concentration of analyte in the sample. For instance, very low
1401
concentration of ethanol in dealcoholized beverages may not be predicted using
1402
nondestructive instrumental techniques. Therefore, future work should focus on improving
1403
nondestructive instrumental techniques while enhancing the efficiency of developed
1404
algorithms leading to improved prediction accuracy of the build model for low concentration
1405
analytes and possible implementation of these techniques at industrial level.
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The current review will open new avenues for the application of advanced green
1407
nondestructive instrumental techniques for monitoring of alcoholic beverages. The future
1408
research should be explored to improve and extend the area of its applications. The academic
1409
and field training should be provided for the implementation of nondestructive instrumental
1410
techniques as analytical tool for monitoring quality parameters of alcoholic beverages. In
1411
order to achieve reproducible discrimination for future applications, many types of flavor and
1412
classes of alcoholic beverage from different countries should be harmonized. The cheaper
1413
and portable nondestructive instrumental techniques are expected to further improve their
1414
accessibility to smaller industries. A single step algorithm is the future of rapid screening of
1415
various compounds having multiple health benefits. The studies should also be focused on in-
1416
line monitoring of alcoholic beverages with wide combinations of chemometric techniques.
1417
The future work is expected to establish valuable algorithms present in other fields for their
1418
implantation in the analytical field of alcoholic beverages.
1419
5. Conclusion
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This review article has primarily outlined guidelines and recent advances in quality
1421
control, discrimination and adulteration detection of alcoholic beverages. The nondestructive
1422
instrumental techniques simultaneously realizing detection of various quality parameters,
1423
monitoring
1424
differentiating different flavor or classes of alcoholic beverages are thoroughly highlighted.
1425
The major advances in the implementation of these techniques for the analysis of alcoholic
1426
beverages have synchronized with the developments in powerful chemometrics. These
1427
methods allow capturing of related information from complex and large data for quantitative
fermentation
attributes,
discrimination
of
geographical
regions,
and
42
1428
prediction and qualitative analysis of alcoholic beverages. However, investigations targeting
1429
availability of nondestructive instrument, development of representative databanks, diversity
1430
of applications, and better understanding of the technology should form the basis of future
1431
research explorations.
1432
Compliance with Ethical Standards:
1433
Acknowledgments The authors appreciatively acknowledge financial support provided by the National
1435
Natural Science Foundation of China [31750110458, 31671844, and 31601543]; National
1436
Key Technology Research and Development Program of China [2018YFD0400803,
1437
2017YFC1600805, 2017YFC1600806, 2016YFD0401104]; and the China Postdoctoral
1438
Science Foundation [2017M611736]. We also would like to thank our colleagues in School
1439
of Food and Biological Engineering who helped in this work.
1440
Conflict of interest
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All the authors declared no conflict of interest.
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Table 1. Application of Vis-NIR, UV-VIS-NIR, and NIR spectroscopy for quantitation, discrimination, and authentication of alcoholic beverages Beverage type
Parameters
Vinho verde wines Dukang base liquor
Quantification of 10 volatile compounds (mg L-1)
Red wines
3-methyl-1-butanol (mg/100mL) 1-butanol (mg/100mL) 1-propanol (mg/100mL) Polyphenolic compounds (mg L-1)
No of sample/ Division/Quantificati on 89-99/, 2/3 Calibration, 1/3 Validation/ GC-MS 81/61:20/ GC-MS
Mode/Wavelen gth range
Pretreatment
Chemome trics
Optimal model
Precision
Reference
Transmission/ 600-14000 cm-1
SNV
PLS
PLS
R2= 0.94-0.97, RMSE=3.9-42.5
(Genisheva, et al., 2018)
Transmittance/ 4000-12000 cm-1
1st Dev, 2nd Dev, SLS, MSC
PLS
PLS
R2=98.05, RMCEC=0.67, R2=95.22, RMSEP=1.35 R2=98.05, RMCEC=0.49, R2=97.96, RMSEP=0.81 R2=95.21, RMCEC=0.27, R2=94.72, RMSEP=0.40
(Han, Zhang, Li, Li, & Liu, 2016)
78/ HPLC
Transmittance/ 190-2500 nm
1st Dev, 2nd Dev, SNV, MSC, Savitzky-Golay smoothing, Smoothing & Normalize -----
PCR, PLS
PLS
r2cal range=0.41-1 RMSEC range=0.11-158.58 RMSECV range=0.14-0.88
(Martelo-Vidal & Vázquez, 2014)
PLSR
PLSR
(Shen, et al., 2010)
PLSR, LSV-SVM
LSVSVM
2nd Dev, SNV
PLS
PLS
rcal > 0.94) for all amino acids except proline, histidine, and arginine rcv >0.81 for 12 amino acids rval=0.915, RMSE=0.168 rval= 0.888, RMSE= 0.146 rval= 0.872, RMSE= 0.033 R2val= 0.90, SECV=9.80 (Calcium) R2val= 0.86, SECV= 0.65 (Iron) R2val= 0.89, SECV= 147.6 (Potassium) R2val= <0.80 (other minerals)
Amino acids (mg/L)
98/ HPLC
Absorbance/ 800-2500 nm
Chinese rice wine
Alcohol content (%) Titratable acidity (g L-1) pH Calcium, Potassium, Magnesium, Phosphorus, Sodium, Sulphur, Iron, Boron and Manganese (mg L-1) Total reducing sugar (g L-1) Amino acid nitrogen (g L-1) pH
138/92:46 Distillation, titration, Potentiometry 126/63:63/ Inductively coupled plasma atomic emission spectrometry 156/117:36/ Colorimetry, titration, pH meter
Transreflectance / 600-1200 nm Transmittance/ 400-2500 nm
Transmission/ 10000-4000 cm-1
1st Dev, 2nd Dev, SNV, MSC
PLS, SiPLS, SVM, SiSVM
SiSVM
Apple wine
Soluble solid content (%) Total acidity (g/100 mL) Total ester content (g/L) pH
Transmission/ 4000–12000 cm−1
1st Dev, 2nd Dev, MSC,
PLS
PLS
Chinese rice wines
Transmission/ 800-2500 nm
-----
Rice wines
Potassium (mg L-1) Magnesium (mg L-1) Zincum (mg L-1) Iron (mg L-1) SSC (˚Brix) pH
380/288:92/ Hand-held refractometer, Titration, pH meter 38/29:9/ Atomic absorption spectroscopy
Transmission/ 325-1075 nm
Savitzky-Golay, SNV
PLS
PLS
Apple wine
Alcohol strength (mL/L) Titratable acidity (g/L) Glucose (g L-1), fructose (g L-1), ethanol (g L-1), Glycerol (g L-1), total phenolics (mg L-1), Total anthocyanins (mg L-1), Total flavonoids (mg L-1), Alcohol content (%), Titratable acidity (g L-1) pH Alcohol content (%), Titratable acidity (g L-1) pH Malvidin-3-glucoside (mg L1 ), pigmented polymers (mg L-1), tannins (mg L-1) Ethanol (%)
Transmission/ 4000–12000 cm−1 Transmission/ 12500-3600 cm-1
2nd Dev, Straight line subtraction SNV, MSC, Derivative transformation
PLS
Red wine
300/240:60/ Refractometer, pH meter 160/120:40/ PT-1 Alcoholmeter, Titration 75/ HPLC, Spectrophotometer
(Cozzolino, et al., 2008)
(Wu, Long, et al., 2015)
oo
-p r
Rcal=0.958, RMSEC=12.10, Rval=0.861, RMSEP=16.90 Rcal=0.885, RMSEC=3.78, Rval=0.700, RMSEP=4.17 Rcal=0.351, RMSEC=0.25, Rval=0.621, RMSEP=0.13 Rcal=0.403, RMSEC=0.73, Rval=0.584, RMSEP=0.58 r= 0.95, SEP=0.16, RMSEP=0.17 r=0.94, SEP=0.02, RMSEP=0.02
(F. Liu, He, Wang, & Pan, 2007)
PLS
Rc2=0.923, RMSECV=4.63, Rp2=0.993, RMSEP=4.25 Rc2=0.931, RMSECV=0.264, Rp2=0.973, RMSEP=0.21
(Peng, Ge, Cui, & Zhao, 2016)
PLS
PLS
Calibration and cross validation range rcal=0.97-0.99, RMSEC=0.41-217, rcv=0.93-0.99, RMSECV=0.44-229,
(Di Egidio, Sinelli, Giovanelli, Moles, & Casiraghi, 2010)
-----
PLSR, LS-SVM
LSSVM
(Yu, Niu, Ying, & Pan, 2008)
-----
PLS, LSSVM
LSSVM
re
PLSR
(Ye, Yue, Yuan, & Li, 2014)
(Yu, Zhou, Fu, Xie, & Ying, 2007)
2nd Dev, scatter correction
PLS
PLS
Transmission/ 350-1200 nm
1st Dev, 2nd Dev
PLS
PLS
Rcal=0.892, RMSEC=0.16, RMSECV=0.29
(Ying, Yu, Pan, & Lin, 2006)
Alcohol degree (%), pH, Total acid (g L-1), amino acid nitrogen (g L-1), ˚brix (%) Total acid (mol/L), Total sugar (g/L), Alcohol content (v/v)
100/ National Standard of China GB/T 136622000 88/ GB/T13662-2000 & GB 17946-2000 official methods 91/ GB/T15038-2006 official method
Transmission/ 800-2500 nm
2nd Dev
PLS
PLS
R2cal =0.83-0.96, r2val=0.77-0.92 Prediction of amino acid nitrogen was the worst & further study needed
(Yu, Ying, Fu, & Lu, 2006)
Transmittance/ 400-2400 nm
1st Dev, 2nd Dev, SNV, MSC, OSC
ACOPLS
R=0.987, RMSE=0.001, R=0.973, RMSE=0.001 R=0.999, RMSE=0.082, R=0.994, RMSE=0.089 R=0.943, RMSE=0.187, R=0.928, RMSE=0.206
(Hu, Yin, Ma, & Liu, 2018)
Apple wine
Detection of volatile compounds (mg/L)
72/42:30 HS-SPME–GC–MS
Transmittance/ 4000-12000 cm-1
PLS
R2c=0.8844-0.9497, RMSEC=0.023-37.50, R2cv=0.8278-0.8916, RMSECV=0.025-49.60
(Ye, Gao, Li, Yuan, & Yue, 2016)
Apple wine
Decanoic acid (mg/L), Hexanoic acid (mg/L), Octanoic acid (mg/L)
72/42:30 HS-SPME–GC–MS
Transmittance/ 4000-12000 cm-1
PLS
PLS
Ethanol content (g/L) Total acid (g/L)
144/108:36 HPLC, titration
Transmittance/ 4000-10000 cm-1
GASVM
Rice wine ‘Makgeoll i’
Alcohol (%), Reducing sugar (%), Titratable acid (%)
132/105-106:26-27 HPLS, Copperbicinchoninate method, Titration
Transmission/ 4000-10000 cm-1
RCA-PLS, SPA-PLS, iPLS, GA, RCASVM SPA-SVM iPLS-SVM GA-SVM PLSR
R2c=0.9007, RMSEC=0.40, R2cv=0.8590, RMSECV= 0.44 R2c=0.9179, RMSEC=0.74, R2cv=0.8680, RMSECV=0.85 R2c=0.9118, RMSEC=0.89, R2cv=0.8539, RMSECV=1.05 R2=0.97, RMSEC=2.42, R2=0.94, RMSEP=3.02 R2=0.97, RMSEC=0.09, R2=0.97, RMSEP=0.10
(Ye, et al., 2016)
Chinese rice wine
1st Dev, 2nd Dev, MSC, VN, MMN, ECO, MSL 1st Dev, 2nd Dev, MSC, VN, MMN, ECO, MSL 1st Dev, SNV, MSC, DT, BL, Savitzky-Golay
PLS, CARSPLS, ACO-PLS PLS
PLSR
R2p=0.973, SEP=0.760% (normalization) R2p=0.945, SEP=1.233% (no data preprocessing) R2p=0.882, SEP=0.045% (normalization)
(Kim & Cho, 2015)
Rice wine
Alcohol content (%) Titratable acidity (g L-1) pH
100/75:25 Distillation/aerometry, titration, potentiometry
500-1200 nm
PLSR, LS-SVM
LSSVM
Rcal=0.963, RMSEC=0.09, rval=0.960, RMSEP=0.11 Rcal=0.952, RMSEC=0.06, rval=0.942, RMSEP=0.07 Rcal=0.959, RMSEC=0.01, rval=0.866, RMSEP=0.02
(Yu, Niu, Ying, & Pan, 2011)
Chinese rice wine
Chinese rice wine
Wine
Transmission/ 800-2500 nm
Jo
Red wine
ur Transmission/ 400-2500 nm
Rice Wine
360-2000 nm
R2= 0.9408, RMCECV= 2.15, R2= 0.9214, RMSEP= 2.07 R2= 0.9356, RMCECV= 0.03, R2= 0.9120, RMSEP= 0.03 R2= 0.9407, RMCECV=0.10, R2= 0.9302, RMSEP=0.10 Rc2= 0.72, RMSECV=1.45, Rp2= 0.76, RMSEP=1.48 Rc2= 0.91, RMSECV=0.05, Rp2= 0.94, RMSEP=0.04 Rc2= 0.72, RMSECV=0.21, Rp2= 0.91, RMSEP= 0.10 Rc2= 0.84, RMSECV=0.11, Rp2= 0.87, RMSEP=0.10
(Yu, et al., 2009)
rcal=0.963, RMSEC=0.09, rval=0.960, RMSEP=0.11 rcal=0.952, RMSEC=0.06, rval=0.942, RMSEP=0.07 rcal=0.959, RMSEC=0.01, rval=0.866, RMSEP=0.02 R2val=0.91, RMSEP=0.09 R2val=0.82, RMSEP=0.05 R2val=0.96, RMSEP=0.01 R2cal>0.80, SEC=3.1-55.7, SECV=3.2-61.7
Rice wine
100/75:25 Distillation, titration, Potentiometry 147/98:49 Distillation, titration, Potentiometry 495/ HPLC
PLSR
lP
Chinese rice wine
na
Australian wines
-----
f
Chinese rice wine
1st Dev, 2nd Dev, Normalization, MSC, SNV, Norris-Gap, Savitzky-Golay -----
(Yu, Lin, et al., 2008)
(Cozzolino, et al., 2004)
(Wu, Xu, Wang, et al., 2015)
Beer
Soluble solids content (%)
360/180:90:90 Refractometer
Transmission/ 325–1075 nm
1st Dev, 2nd Dev, SG, SNV
Shaoxing & Chinese rice wines Chinese liquors
Fraudulent detection
54/36:18
Transmission/ 800-2500 nm
2nd Dev, SNV, MSC
Authenticity & adulteration 42 samples without marked age 78 samples with marked age Fermentation stage classification (0-3, 4-6, 7-9, & 10-20 days Brands, alcohol degree, age & six flavors
120/78:42
Transmission/ 4000-12000 cm-1
-----
156/117:36
Transmission/ 10000-4000 cm-1
730/488:244
Absorbance/ 570-1848 nm
Wine
Australian and New Zealand origin classification
64/34:30
Whiskey, brandy, rum & vodka White and red wines
Adulterated with 5% and 10% (v/v) of water, ethanol or methanol
69/40:24:5
Transmission/ Vis & NIR 400– 2500 nm; UV-Vis MIR 400-4000 cm-1 1100 to 2500 nm
Denomination of origin, 82 white wine 153 red wine
235/
Absorbance/ 200 to 800 nm
Chinese rice wines Shiraz wines
Discrimination of geographical origin Four Western Australia and Coonawarra were separated from other wines
38/29:9/
Transmission/ 800-2500 nm Transmission/ 400-2500 nm
Chinese rice wines
Discrimination of geographical origins
112/84:28
Absorbance/ 190–800 nm
Whisky
Discrimination of whisky brands and counterfeit identification Discriminate between commercial white wines of different varietal origins
237/158:79
Absorbance/ 190-1100 nm
1st Dev, Savitzky-Golay
269/136:133
Transmission/ 400-2500 nm
Classify the geographical origin of wines from Australia and Spain Fermentation monitoring (0, 2, 5,7 and 30–35 days)
63/32:31
2nd Dev, smoothing, Savitzky-Golay derivation 2nd Dev, SNV,
Beer
Qualitative characterization of five aging categories of beer
139
Rice wine
Discrimination of rice wine age (1, 3, & 5) Classify wines from Australia, New Zealand, France & Germany Discrimination inside a controlled designation of origin
Riesling wines Wine
DA, DPLS
Calibration= 97.2%, Validation= 100%
(Shen, et al., 2012)
PCA, SVM
SVM
Accuracy, sensitivity and specificity were 94.9%, 93.1% & 97.9%, respectively
(H. Chen, Tan, Wu, Wang, & Zhu, 2014)
1st Dev, 2nd Dev, SNV, MSC
PCA, PLSDA
PLS-DA
Calibration=92.3%, Prediction=89.7%
(Wu, Long, et al., 2015)
cubic-order Savitzky-Golay smoothing 4, 8, 16, and 32 points, 1st Dev, 2nd Dev, -----
SVM, SIMCA, PCA-LDA
PCALDA
Discrimination range 95.65–100 % in training and test set
(Li, et al., 2014)
PCA, SIMCA, PLS-DA
PLS-DA
Overall classification UV–VIS=67 NIR=76 MIR=90
(D Cozzolino, WU Cynkar, N Shah, & PA Smith, 2011a)
PCA SIMCA
SIMCA
100% of correct prediction, at a confidence level of 95%
(Pontes, et al., 2006)
SVM, SIMCA, PLS-DA, NN-MLP, & k-NN PCA, PLSR PCA, LDA, SIMCA
SVM
Classification rates above 96% achieved
(Acevedo, Jiménez, Maldonado, Domínguez, & Narváez, 2007)
LDA, PLS-DA, SVM, SIMCA PLS-DA
LDA
Training set= 98.96%, Testing set=100%
(Wu, Li, et al., 2015)
PLS-DA
Classification rates for genuine and false samples over 98.6% and 93.1%, respectively
PCA, PCR, DPLS
DPLS
Correctly classified 100% of Riesling & up to 96% of Chardonnay wines
(Martins, Talhavini, Vieira, Zacca, & Braga, 2017) (Cozzolino, Smyth, & Gishen, 2003)
PCA, LDA, PLS-DA PCA, LDA
PLS-DA
Correctly classified 100% and 84.7% of the Australian and Spanish wine samples, respectively
LDA
Correctly classified range 78.6 to 100% in calibration and prediction set
SELEC T+LDA
Correct predictions range 62-86%
(GhasemiVarnamkhasti & Forina, 2014)
LSSVM SLDA
Correctly classified range 93.75-100%
(Yu, Lin, et al., 2008)
SLDA calibration models correctly classified 86%, 67%, 67% and 87.5% of the Australian, New Zealand, French and German Riesling wines, respectively Condado Salnés and Ribeira de Ulla was 100% classified by applying SIMCA Rosal wines was 100% classified by applying LDA
(L. Liu, et al., 2008)
lP
Transmission/ 400-2500 nm
75
Transmission/ 12500-3600 cm-1 12500-5405 cm-1
147/98:49 50
33
oo
(Yu, et al., 2007)
LDA
NIR range 1300-1650 nm provide best results with 100% classification Overall correct classification rate 60%
re
2nd Dev, SNV, Savitzky-Golay derivation & smoothing 1st Dev, 2nd Dev, SNV, MSC & 9 points smooth
PLSR
-p r
-----
SNV, MSC, Derivative transformation -----
Transmission/ 800-2500 nm transmission/ 400-2500 nm
-----
Transmission/ 190-2500 nm
2nd Dev, SNV, Smoothing, Savitzky-Golay
-----
f
(F. Liu, Jiang, & He, 2009)
na
Riesling & chardonna y wine Tempranil lo red wines Red wine
98
r=0.9905, RMSEC=0.11, r=0.9915, RMSEV=0.10, r=0.9818, RMSEP=0.16
Savitzky-Golay second-order polynomial fitting -----
ur
Chinese liquor
SPALSSVM
Jo
Chinese rice wine
SPA, RCA, ICA, PLS, MLR, LSSVM PCA, DA, DPLS
PCA, LDA, StepLDA, KNN,GA, SELECT PCA, DA, LS-SVM PCA, PLSDA, SLDA PCA, LDA, SVM, SIMCA
LDA, SIMCA
(Riovanto, Cynkar, Berzaghi, & Cozzolino, 2011)
(L. Liu, Cozzolino, Cynkar, Gishen, & Colby, 2006) (Di Egidio, et al., 2010)
(Martelo Vidal, Domínguez Agis, & Vázquez, 2013)
Table 2. Application of MIR spectroscopy for quantitation, discrimination, and authentication of alcoholic beverages Beverage type
Parameters
Red wines
TPI280 Folin-Ciocalteu index glories color parameters CIELab color parameters
Straw wine
Alcohol content (%), Sugar content (g/L), Titratable acidity (g/L)
Cabernet sauvignon wines
Total phenolic (mg GAE/L), Anthocyanins (mg malvidin-3glucoside/L), Tannins (g cyanidin/L), Flavonoids (mg GAE/L), ABTS & DPPH assays (mmol
No of sample/division /Quantification 36/ Spectrophotometer, Color meter
302/244:58 115/90:25 107/87:20/ Distillation, HPLC, 30/24:6 Spectrophotometer
Mode/Wavelength range
Pretreatment
Transmittance/ 4000-400 cm-1
-----
Chemometrics
PLS-1
Optimal model PLS-1
Transmission/ 1000-300 cm-1
OSC
PLSR
PLSR
4000-550 cm-1
1st Dev, 2nd Dev, SNV, MSC, Savitzky-Golay
PLS1, PLS2
PLS1
Precision
Reference
Rc2=0.9195, RMSEc=2.22, Rp2=0.8908, RMSEp=2.70 Rc2=0.9029, RMSEc=2.19, Rp2=0.8538, RMSEp=2.81 Rc2>0.93, RMSEc<1.11, Rp2>0.91, RMSEp<1.32 Rc2>0.92, RMSEc<2.17, Rp2>0.90, RMSEp<2.49 R2pred=0.99, RMSEP=0.28 R2pred=0.98, RMSEP=9.9 R2pred=0.92, RMSEP=0.29
(Garcia-Hernandez, SalvoComino, Martin-Pedrosa, GarciaCabezon, & Rodriguez-Mendez, 2020)
Rv2=0.9301–0.9502 Rc2=0.9389-0.9480
(Grijalva-Verdugo, HernándezMartínez, Meza-Márquez, Gallardo-Velázquez, & OsorioRevilla, 2018)
(Croce, et al., 2020)
PLS, iPLS, SVM, ISVM
ISVM
Ethanol (%)
57/ GC FID
1200-850 cm-1
1st Dev, 2nd Dev, Normalization, Mean centering
PLS
PLS
Methanol (g/hL), Acetaldehyde (g/hL), Fusel alcohols (g/hL), Ethyl acetate (g/hL), Alcoholic strength (%) Total reducing sugar (g L-1) Amino acid nitrogen (g L-1) pH
52-166/41-83:42-83/ GC-FID, Distillation
4000-400 cm-1
1st Dev, 2nd Dev, MSC, SLS
PLS
PLS
156/117:36/ Colorimetry, Titration, pH meter
Transmission/ 4000-800 cm-1
1st Dev, 2nd Dev, SNV, MSC
PLS, SiPLS, SVM, SiSVM
SiSVM
Glucose (g L-1), fructose (g L-1), ethanol (g L-1), Glycerol (g L-1), total phenolics (mg L-1), Total anthocyanins (mg L-1), Total flavonoids (mg L-1) Alcohol (%), Reducing sugar (%), Titratable acid (%)
75/ HPLC, Spectrophotometer
700-4000 cm-1
SNV, MSC, Derivative transformation
PLS
PLS
129/96:33 HPLC, Copperbicinchoninate method, titration 100 chloride/ 125 sulfates/ UV/Vis Spectrophotometer 79/52:27 UV-visible Spectrophotometer, pH differential method, Refractometer, Titration, pH meter, 79/52:27 UV-visible Spectrophotometer, Conductivity meter, HPLC, pH meter, Refractometer 80/60:20 Chinese National Standard GB-13662–2008 130/100:30
Transmittance/ 400-4000 cm-1
1st Dev, 2nd Dev, Norris-Gap, Savitzky-Golay
PLSR
PLSR
R2=0.984, SEP= 0.595 R2=0.983, SEP=0.579 R2=0.936, SEP=0.026
f
(Kim, et al., 2016)
Absorbance/ 1000-3050 cm-1
1st Dev, 2nd Dev, SNV, MSC, OSC, SavitzkyGolay 1st Dev, 2nd Dev, Wavelet compression
PLS
PLS
R2p=0.83, RMSEP=0.18 R2p=0.98, RMSEP=0.11
(dos Santos, Páscoa, Porto, Cerdeira, & Lopes, 2016)
PLS
R2cal=0.71-0.98, RMSEC=0.02-208.08 R2val=0.54-0.92, RMSEP=0.02-312.43
(Ozturk, Yucesoy, & Ozen, 2012)
Wine
Total phenol (mg/L), Anthocyanin (mg/L), brix (%), titratable acidity (g/L), pH, color intensity,
Raki
Total phenol (mg/L), Electrical conductivity (μS/cm), sugar (ppm), pH, Brix (%)
Chinese rice win
Alcohol degree (%), total sugar (g/L), non-sugar solid (g/L), total acid (g/L), pH Alcohol (%), specific gravity, pH, titratable acidity (g/L), volatile acidity (g/L), glucose + fructose (g/L) Total sugar(g/L), nonsugar solid (g/L), glucose (g/L), Isomaltose (g/L), Isomaltotriose (mg/L), Maltose (g/L), panose (g/L), Total acid (g/L), amino acid nitrogen (g/L), pH, Lactic acid (g/L) Total phenolics (mg L-1 gallic acid), total anthocyanins (mg L-1 malvidin-3-glucoside), Condensed tannins (mg L-1 catechin) Ethanol (%), Sucrose (%), Tartaric acid (%)
Red & white wine
Chinese rice wine
Red wine
Gin, rum, vodka, etc.
650-4000 cm-1
650-4000 cm-1
1st Dev, 2nd Dev, Wavelet compression
400-4000 cm-1
4000-375 cm-1
r2= 99.4 RMSECV=32.4, r2=98.2, RMSECV=10.4 r2=97.4-94.1, RMSECV=1.65-10.7 r2=97.1, RMSECV=24.3 r2=97.2, RMSECV=0.37 R2=0.9614, RMCECV=1.37, R2=0.9594, RMSEP= 1.68 R2=0.9548, RMCECV=0.02, R2=0.9434, RMSEP= 0.03 R2=0.9311, RMCECV=0.13, R2=0.9195, RMSEP=0.11 Calibration and cross validation range rcal=0.96-0.99, RMSEC=0.39-226, rcv=0.91-0.99, RMSECV=0.42-245,
oo
Chloride (g L-1) Sulfate (g L-1)
R2=0.97, RMSECV=1.93, R2=0.96, RMSEP=2.07 R2=0.97, RMSECV=2.64, R2=0.97, RMSEP=2.35 R2=0.95, RMSECV=0.08, R2=0.93, RMSEP=0.09 R2=0.96, RMSECV=0.01, R2=0.95, RMSEP=0.02 R2=0.999, RMSECV=0.0008, R2=0.999, RMSEP=0.1
-p r
Wine
PLS
re
Makgeolli wine
1st Dev, 2nd Dev, SNV, MSC, Savitzky-Golay
lP
Red wine
4000-400 cm-1
(Wu, Xu, Long, Zhang, et al., 2015)
(Debebe, Redi-Abshiro, & Chandravanshi, 2017)
(Anjos, Santos, Estevinho, & Caldeira, 2016)
(Wu, Long, et al., 2015)
(Di Egidio, et al., 2010)
PLS
PLS
R2cal= 0.99-0.99, RMSEC=0.07-523.60 R2val=0.76-0.98, RMSEP=0.10-464.70
(Ozturk, et al., 2012)
PLSR, SMLR, ANN
PLSR
R2c=0.817-0.978, RMSECV=0.133-3.310 R2v=0.789-0.968, RMSEP=0.076-2.740
(Shen, Wu, Wei, Liu, & Tang, 2017)
-----
PLS
PLS
R2=0.85-0.96, SECV=0.001-1.40 R=0.65-0.99, SEP=0.0007-1.35
(D Cozzolino, W Cynkar, N Shah, & P Smith, 2011b)
na
Chinese rice wine
156/117:39 HPLC, 3,5-dinitrosalicylic acid colorimetry method
1st Dev, 2nd Dev, Smoothing, MSC, SNV
ur
Ethiopian traditional alcoholic beverages Grapederived spirits
TE/L) Total sugar (g/L), Ethanol (g/L), Total acid (g/L), Amino nitrogen (g/L)
Jo
Chinese rice wine
90/41-66:15-20 HPLC, Chinese National Standard GB 13662–2008
Transmission/ 950-3300 cm-1
-----
PLSR
PLSR
rcal=0.821-0.991, RMSECV=0.046-50.6 rval=0.488-0.990, RMSEP=0.029-36.3
(Shen, Ying, Li, Zheng, & Hu, 2011)
600/400:200 UV-vis Spectroscopy
Transmission/ 979-2989 cm-1
-----
PLS
PLS
(Fragoso, Acena, Guasch, Mestres, & Busto, 2011)
100 Synthetic sample
600–4000 cm-1
-----
PLS
PLS
PLS
PLS
R2cal>0.95, R2val>0.95 based on selected region R2cal>0.93, R2val>0.91 based on selected region R2cal>0.82, R2val>0.72 based on selected region R2=0.9910, RMSECV=0.20, R2=0.9896, RMSEP=0.21 R2=0.9962, RMSECV=0.10, R2=0.9956, RMSEP=0.11 R2=0.9898, RMSECV=0.16, R2=0.9888, RMSEP=0.16 rcal=0.979, RMSEC=0.40, rval=0.949, RMSEC=0.50
PLS
PLS
R2=0.940-0.999, SECV=0.0007-40.8 R2=0.901-0.981, SEP=0.0013-47.30
(Lachenmeier, 2007)
R2cal>0.93, RMSEC=0.6-1090.0, RMSECV=16.7-2950.0, RMSEP=0.2-139.0 R2cal>0.85, RMSEC=0.0-0.7, RMSECV=0.2-1.9, RMSEP=0.0-0.9 R2cal=0.8167-0.9835, RMSEC=3.92-119.03
(Preserova, Ranc, Milde, Kubistova, & Stavek, 2015)
Beer
Attenuation limit (%)
40/30:10 European brewery convention method
4000-600 cm-1
Beer, spirit drinks
Telative density, alcohol (%), original gravity, pH, lactic acid (mg/L), bitterness unit, EBC color, quotient after weber, extinction after weber
926-5012 cm-1
Red wine, rose wine, white wine
Total phenolic compound, Total antioxidant activity
535 spirit drinks 461 beers/ Oscillation-type Densimetry, Refractometry, gas chromatography, Enzymatic analysis, Photometry 25/ UV-visible Spectrophotometer,
1st Dev, 2nd Dev, SNV, MSC, Baseline correction -----
4000-600 cm-1
1st Dev, 2nd Dev,
PLS
PLS
Chinese
17 Free amino acid
116/87:29
4000-400 cm-1
1st Dev, 2nd Dev,
PLS, SiPLS,
GA-
(Nagarajan, Gupta, Mehrotra, & Bajaj, 2006)
(Castritius, Geier, Jochims, Stahl, & Harms, 2012)
(Wu, Xu, Long, Wang, et al.,
GA-SiPLS
SiPLS
R2pre=0.8074-0.9744, RMSEP=4.21-130.56
2015b)
PCA
PCA
PC1, PC2 and PC3 explaining 99.8%total variance between samples
(Garcia-Hernandez, et al., 2020)
SNV, SavitzkyGolay, Smoothing, 2nd Dev
PCA, LDA
LDA
Overall classification rate 93%
(Yadav & Sharma, 2019)
PCA, PLS-DA
PLS-DA
(Gordon, et al., 2018)
Transmission/ 4000-400 cm-1
-----
Model 1-4, Model 5-8
Model 2 Model 8
Correct classification 100% for commercial & craft Correct classification 100% for lager Correct classification 98% for dark & pale ale Discrimination rate 100% per origin Discrimination rate 99.0% per time of maturation
156/117:36
Transmission/ 4000-800 cm-1
1st Dev, 2nd Dev, SNV, MSC
PCA, PLS-DA
PLS-DA
Calibration=94.0%, Prediction=94.9%
(Wu, Long, et al., 2015)
98
Transmission/ 400-4000 cm-1
PCA, LDA, SIMCA
SIMCA
Overall correct classification rate 93.02%
(Riovanto, et al., 2011)
75
700-4000 cm-1
PCA, LDA
LDA
Correctly classified 100% in calibration and prediction set
(Di Egidio, et al., 2010)
496
Transmittance/ 5011-929 cm-1
2nd Dev, SNV, Savitzky-Golay derivation & smoothing SNV, MSC, Derivative transformation -----
PCA, LDA
LDA
(Louw, et al., 2009)
17 whisky & 14 caramel colorants
600-1800 cm-1
150/15:135
400-4000 cm-1
Correct classification 98.3% with MIR spectra (red cultivar), whereas, combination of spectra and volatile compounds correctly classify 86.8% (white cultivar) Correctly differentiate between different caramel colorants and whisky samples Prediction accuracy >97.0%
151/85:43:23
Transmission/ 4000-400 cm-1
172
Transmission/ 400-4000 cm-1
36
Transmittance/ 4000-400 cm-1
75
600-3000 cm-1
48
4000-400 cm-1
Discrimination and authentication of whiskies from Scotland, Ireland & USA based on origin & time of maturation (2-12 year) Fermentation stage classification (0-3, 4-6, 7-9, & 10-20 days Four Western Australia and Coonawarra were separated from other wines Fermentation monitoring (0, 2, 5,7 and 30–35 days) Characterization of young white & red wine cultivar using MIR and GC-MS Detection of counterfeit samples for authenticity
100 Scottish, 50 Irish % USA/25% validation
Detection of lethal fake liquors Discrimination of Cognacs and other distilled drinks (armagnacs, whiskies, brandies, bourbons, rums, and counterfeit products) Classify commercial wines sourced from organic & non-organic production system
Beers
Whisky
Chinese rice wine Shiraz wines
Red wine
South African wine Scotch whisky Liquors Cognacs & noncognacs drinks
IDWT, -----
PCA
PCA
PCA, LS-SVM
LS-SVM
PCA, PLS-DA
PLS-DA
Correctly classified 95% of samples in test set
-----
PCA, DPLS, LDA
DPLS
Classification rate for organic (100%) and non-organic (88%) for white wine Classification rate for organic (73%) and non-organic (85%) for red wine
ur
na
Organic and nonOrganic wines
-----
lP
Illicit liquors
oo
Discriminate wines according to their phenolic content Classification of geographical origin of India Confirm their identity (e.g. ale vs lager, commercial vs craft beer)
-p r
Red wines
(Sujka & Koczoń, 2018)
(McIntyre, Bilyk, Nordon, Colquhoun, & Littlejohn, 2011) (D. Chen, Tan, Huang, Lv, & Li, 2019) (Picque, et al., 2006)
re
(mg/L)
f
SNV, MSC, Savitzky-Golay de-trending, -----
rice wine
(Cozzolino, Holdstock, Dambergs, Cynkar, & Smith, 2009)
Table 3. Application of Raman spectroscopy for quantitation, discrimination, and authentication of alcoholic beverages Parameters
No of sample/division /Quantification
Wavelength range/Laser
White wine
Alcoholic strength (%), total sugars (g/L), total acidity (g/L), volatile acidity (g/L), pH, Density (g/mL)
3500-57 cm-1 1064 nm laser source
1st Dev, 2nd Dev, SNV, Savitzky– Golay, Normalization
Chinese rice wine
Ethanol content (g/L), Total sugar (g/L), Total acid (g/L), Ph
200-2000 cm-1
Chinese rice wine
Ethanol content (%)
108/75:33 Distillation & densimetry, enzymatic method with spectrophotometry, Densimetry, Potentiometric titration, Potentiometry 112/83:28 HPLC, 3,5Dinitrosalicylic acid colorimetry, titration pH meter 30/ Gravimeter method
Chinese rice wine
Ethanol (g/L) Glucose (g/L)
156/117:39 HPLC
200-3000 cm-1 785 nm laser source
1st Dev, 2nd Dev, SNV, MSC, Baseline correction, Detrending Calibration and background removal was done using OMINC software 1st Dev, SNV, MSC, de-trending, Smoothing, Savitzky–Golay
Wine
Alcohol (v/v) Sugar (g/L)
40/ HPLC
4000-400 cm-1
Red wine
Polyphenols (mg/lt), Anthocyanins (mg/lt), Tannins (mg/lt)
114/76:38 Spectrophotometer,
0-2500 cm-1
Wine
Sugar (g/L), Ethanol (%), Glycerol (%)
85/35:50 HPLC
400-4000 cm-1
Apple
Polyphenolic content (mg
Spectrophotometer,
400-4000 cm-1
Jo
Beverage type
50-3350 cm-1 785 nm laser excitation
Pretreatment
1st Dev, baseline, SNV, MSC, DOSC, Savitzky– Golay Average & dark subtraction, Savitzky–Golay, Mean centering, Rolling circle SNV, MSC, Smoothing, Baseline correction Savitzky–Golay, Normalized
Chemome trics
Optimal model
Precision
Reference
PLS
R2p=0.592-0.967, RMSECV=0.0005-0.912, RMSEP=0.0005-0.845, RMSEC=0.0004-0.71
(dos Santos, et al., 2018)
PLS, SiPLS
SiPLS
R2=0.97, RMSECV=1.88, R2=0.95, RMSEP=2.40 R2=0.91, RMSECV=1.39, R2=0.90, RMSEP=1.54 R2=0.94, RMSECV=0.17, R2=0.93, RMSEP=0.19 R2=0.95, RMSECV=0.04, R2=0.93, RMSEP=0.05
(Wu, Long, et al., 2016)
PLS
PLS
R2=0.9258, RMSEC=0.434, RMSEP=0.514
(Yang & Ying, 2011)
PLS, CARSPLS, SVM, CARSSVM PLS
CARSSVM
R2=0.97, RMSECV=3.93, R2=0.97, RMSEP=4.07 R2=0.97, RMSECV=2.75, R2=0.97, RMSEP=2.90
(Wu, Xu, Long, Wang, et al., 2015a)
PLS
r2=0.9982, RMSECV=0.0042, RMSEP= 0.0036 r2=0.9986, RMSECV=0.0030, RMSEP=0.0032
(Q. Wang, Li, Ma, & Si, 2015)
PLS
PLS
(Gallego, Guesalaga, Bordeu, & González, 2010)
PCA, PLS
PCA-PLS
Rc2=0.982, RMSEC=1.81, Rv2=0.829, RMSEV=5.55 Rc2=0.991, RMSEC=8.86, Rv2=0.844, RMSEV=38.5 Rc2=0.995, RMSEC=1.76, Rv2=0.895, RMSEV=8.65 R2=0.995, RMSECV=0.17, RMSEP=0.22 R2=0.999, RMSECV=0.09, RMSEP=0.03 R2=0.980, RMSECV=0.40, RMSEP=0.20
PLS-1
PLS-1
R2C=0.9062, RMSEC=42.4, R2P=0.7933,
(Śliwińska, et al.,
PLS
(Q. Wang, Li, Ma, & Liang, 2014)
GA/L), density (g/cm3)
Density meter
Wine
Classification of wines, from two sorts; Feteasca Regala & Sauvignon Blanc, produced in three Romanian viticulture regions, during five consecutive vintages Discrimination of fermentation stages
30
-1000-3600 cm-1
156/117:39
200-3000 cm-1 785 nm laser source
Wine
Discrimination of lactic acid bacteria
456
3400–200 cm-1 532 nm laser emission
Chinese rice wine
Classification to predict the different wineries
112/83:28
200-2000 cm-1
Malt Scotch whisky
Counterfeit & adulterate discrimination
44
200-3500 cm-1 785 nm laser excitation
Tequilas
Discrimination of silver and aged tequilas
15
450-4000 cm-1
Mezcal
Discriminate between mezcal samples with different aging time
33
200-1800 cm-1 785nm laser source
2016)
(Magdas, Guyon, Feher, & Pinzaru, 2018)
-----
SLDA
SLDA
1st Dev, SNV, MSC, de-trending, Smoothing, Savitzky-Golay Polynomial Subtraction, SNV
PCA, DPLS
DPLS
Correct classification 94.9% in both calibration & prediction set
(Wu, Xu, Long, Wang, et al., 2015a)
PCA, SVM
SVM
Species of Pediococcus & Lactobacillus; & strains of O. oeniand P. damnosuswere were classified with high sensitivity between 86-90 and 84-85%, respectively
(Rodriguez, Thornton, & Thornton, 2017)
PCA, SIMCA, LDA
LDA
Correct classification 98.82% in calibration set Correct classification 100% in prediction set
(Wu, Long, et al., 2016)
PCA, PLSR
PCA, PLSR
(Kiefer & Cromwell, 2017)
PCA
PCA
PCA was sensitive to the cask type in which a whisky was matured and/or finished PLS distinguish between whiskies containing natural colors only or with artificial colorants, age difference, alcohol content & processing Total variance represented by first three PCs were 80%, 10% & 9%, respectively for discrimination
1st Dev, 2nd Dev, SNV, MSC, Baseline correction, Detrending -----
Normalization
oo
-----
PCA, PLSDA
PCA, PLS-DA
Matured mezcal with different aging time was successfully classified
-p r
Chinese rice wine
RMSEP=67.5 R2C=0.9623, RMSEC=0.00, R2P=0.8566, RMSEP=0.00 Discrimination rate of wine variety and geographical origin of 100% in both initial and cross-validation was achieved
f
liqueurs
(Frausto-Reyes, Medina-Gutiérrez, Sato-Berrú, & Sahagún, 2005) (Elías, et al., 2015)
Table 4. Application of e-tongue for quantitation, discrimination, and authentication of alcoholic beverages
TPI280 Folin-Ciocalteu index glories color parameters CIELab color parameters Ferulic acid (µM), Gallic acid (µM), Sinapic acid (µM)
Beer
94/71:23
Lager beer
Alcohol strength (% vol) Color (EBC unitcolor)
Cava wine
Total sugar (g L-1) Total dry extract (g L-1)
Rice wine
Age prediction (one, three, & five year) Prediction of flavor, Marked age, Total soluble solid (%) Prediction of geographical origin Polyphenolic content (mg GA/L), Density (g/cm3) Real extract (Plato◦), Alcohol volume (%), Bitterness (EBU), Polyphenols (mg/L),
25 UV-Vis Spectrophotometer EBC=25׃×A430 63/48:15 FTIR with wine Scan (sugar), from Specific density and alcohol (dry extract) 120/96:24
Apple liqueurs Beer
-1000 to 1000 mV
-----
-----
Windowed slicing integral method, pruning step -----
ANN
ANN
ANN
ANN
r=0.995, RMSE=0.21, r=0.999, RMSE=0.13
Bell-shaped windowing, Compression method Fast Fourier transform
PLS
PLS
R2=0.9751 R2=0.9869
ANNs
ANNs
r=0.992, r=0.952 r=0.991, r=0.938
PLS, BPANN PLSR, SVM-LOO, SVM-TF ELM
PLS, BPANN PLSR, SVMLOO, ELM
R2=0.999, RS=0.004 (PLS) R2=0.999, RS=0.007 (BP-ANN) R2=0.9952 (PLSR), R2=0.9568 (SVM-LOO), R2>0.95 (SVM-LOO) R2=0.9647, RMSE=0.26, R2=0.9436, RMSE=0.36
PLS-1
PLS-1
R2C=0.9767, RMSEC=29.75, R2P=0.9396, RMSEP=47.74 R2C=0.9252, RMSEC=0.001, R2P=0.8783, RMSEP= 0.001
-----
-0.6 to 1 V
ur
Alcohol content
Rice wine
Pretreatment
98/75:23
Beer
Rice wines
Potential
-1.0 to +1.3 V
Jo 275/150:125
Chemomet rics
0 to 0.5 V -----
200
0 to 1.5 V
Spectrophotometer, Density meter
-1.0 to +1.0 V
Optimal model
re
Red wines
No of sample/ division/ Quantification 36/ Spectrophotometer, Color meter
PLS-1
lP
Parameters
na
Beverage type
---------
----Normalized
PLS-1
(Cetó, GutiérrezCapitán, Calvo, & del Valle, 2013) (Blanco, De la Fuente, Caballero, & Rodríguez-Méndez, 2015) (Cetó, Capdevila, Puig Pujol, & del Valle, 2014)
(Zhenbo Wei, Wang, & Ye, 2011) (ZhenBo Wei, Wang, Cui, & Wang, 2016) (J. Wang, Zhu, Zhang, & Wei, 2019) (Śliwińska, et al., 2016)
PLS
PLS
R=0.90, RMSEC=0.60, R=0.76, RMSEP=0.78, R=0.85, RMSEC=0.88, R=0.70, RMSEP=1.10, R=0.93, RMSEC=2.10, R=0.89, RMSEP=2.50, R=0.83, RMSEC=46.0, R=0.81, RMSEP=52.0,
(Polshin, et al., 2010)
-----
-----
PLSR
PLSR
r2>0.31, RMSEP<0.52 r2>0.61, RMSEP<79.04 r2>0.48, RMSEP<327.06 r2>0.54, RMSEP<37.20
(Kang, Lee, & Park, 2014)
PLS
PLS
(Cetó, et al., 2017)
PLS
PLS
R=0.982, NRMSE=0.048, r=0.969, NRMSE=0.077 R=0.981, NRMSE=0.045, r=0.917, NRMSE=0.110 R2>0.742 (training set) R2>0.646 (test set)
R=0.965, RMSE=15.1, r=0.896, RMSE=26.6 R=0.977, RMSE=0.55, r=0.858, RMSE=1.52 R=0.895, RMSE=0.49, r=0.791, RMSE=0.74 R=0.993, RMSE=9.50, r=0.886, RMSE=41.4 R=0.940, RMSE=2.36, r=0.755, RMSE=4.89 PC1, PC2 and PC3 explaining 85.8% total variance between samples Correct classifications from individual samples was
(Cetó, Capdevila, Mínguez, & del Valle, 2014)
Wine
Ageing time, Global sensory scores (0-10) Process duration, ethyl alcohol content, density, extract content, Content of reducing sugars Folin index (mg L-1), I280 index (arb. unit), I320 index (arb. unit), Tannins (mg L-1), Anthocyanins (mg L-1) Discriminate wines according to their phenolic content Märzenbier style, Lager,
52
-1.0 to +1.3 V
33
-0.5 to 1.5 V
Fast Fourier transform, GAs -----
20/ Acid butanol assay, UV/VIS Spectrophotometer
-0.4 to 0.8 V
-----
PLS1, ANN
ANN
36
-1000 to 1000 mV -----
-----
PCA
PCA
LDA
LDA
Beer
(Cetó & del Valle, 2014)
-----
9×3 batches Sensory evaluation HPLC, LC–MS/MS
Red wines
(Garcia-Hernandez, et al., 2020)
-----
Sensory attributes, 3-sugars (mg/L), 5-organic acids (mg/L), 9-amino acids (mg/L)
Rosé cava wines
Rc2=0.9343, RMSEc=2.01, Rp2=0.8956, RMSEp=2.60 Rc2=0.9276, RMSEc=1.89, Rp2=0.8944, RMSEp=2.34 Rc2>0.97, RMSEc<0.53, Rp2>0.94, RMSEp<0.86 Rc2>0.97, RMSEc<0.92, Rp2>0.94, RMSEp<1.38 r=0.994, r=0.977, r=0.986 r=0.998, r=0.988, r=0.981 r=0.994, r=0.978, r=0.976
Reference
119/79:40
Korean rice wines
Wine
Precision
94
Windowed
(Kutyła Olesiuk, Wesoły, & Wróblewski, 2018)
(Garcia-Hernandez, et al., 2020) (Cetó & del Valle,
Pilsen, Black beer, low alcohol & Alsacien style beer discrimination Classification of geographical origin
Rice wine
200
0 to 1.5 V
slicing integral method, pruning step -----
-----
-----
81.9%
2014)
PCA, LLP, LDA
LLP,
Successfully classified geographical origin with total variance of 99.81% (LLP1 89.88%, LLP2 9.93%),
(J. Wang, et al., 2019)
PCA, LDA
LDA
(Cetó, et al., 2013)
PCA, LDA
LDA
LDA explained variance for first two discrimination function was 94.4% and correct classification rate was 81.9% Correct classification rate according to beer style was 100%
PCA, LDA
LDA
Correct classification rate 100%
Beer
Discrimination of different commercial beer types
51
Lager beer
Discrimination of various types of beer
25
-0.6 to 1 V
Cava wine
Classification based on different ageing times
65
-1.0 to +1.3 V
Italian barbera wines Chinese rice wine
Classify four types of barbera wines
53
-----
-----
PCA, LDA, CART
LDA
Correct classification rate 98.1%
Discriminate the samples from different vintage years based on 28 taste-active compounds and the sensory attributes Differentiate chinese rice wine of different marked ages (one, three, & five year) Classify according to four different marked ages (3, 5, 8 and 10 years) for authentication
32 Sensor evaluation, HPLC
-----
-----
LDA, PLSDA
LDA, PLS-DA
Build model successfully discriminate the sample sourced from different vintage year (2003, 2005, 2008, & 2010)
(Cetó, Capdevila, Puig Pujol, et al., 2014) (Buratti, Benedetti, Scampicchio, & Pangerod, 2004) (Yu, Zhao, Li, Tian, & Ma, 2015)
120
0 to 0.5 V
-----
PCA, CA
PCA
Configuration of location of each sample was clear in PCA score plot with total variance of 96.92%
(Zhenbo Wei, et al., 2011)
120/80:40
-0.5 to 1.6 V
Normalization,
BPANN
Classification rate was 100% in calibration & prediction set
(Ouyang, Zhao, & Chen, 2013)
Beer
Identification of three style of beer (lager, stout, & IPA)
25
-1.0 to 1.2 V
Discrete wavelet transforms
SIMCA, PLS-DA, KNN, BPANN, SVM LDA
Chinese rice wine Rice wines
Discrimination of seven classes Discrimination of three type of wine Brand uniformity control (Primitivo & Negroamaro)
72/51:21
-----
-----
165
-----
-----
Discrimination & identification of wine Discrimination of the effect of the barrel in wine maturing & ageing time Qualitative assessment of wine production process Classification of wine samples according to aging time Discrimination between dark & pale beer
1599 red wine 4898 white wine 52
-1.0 to +1.3 V
33
-0.5 to 1.5 V
78
+1.01 to +1.31 V
Discriminate between expensive & cheap whisky samples; & to detect adulteration processes Differentiation of fermentation time
12
Wine Cava wine
Beer
Wines & whiskies
Pomegranate wine
21
DFA worked better in classifying three types of wine
PCA, SIMCA, PLS-DA PCA, ANN
PLS-DA
Correct discrimination of more than 70%
ANN
Classification and identification rate 98%
Fast Fourier transform, GAs
PCA, LDA
LDA
Classification rate 100% of the type of barred used during maturation process
-----
PCA, PLSDA LDA
PCA, PLS-DA LDA
Higher rate of classification was observed using hybrid electronic tongue Correct classification between 80 to 96%
(Kutyła Olesiuk, et al., 2018) (Giménez-Gómez, et al., 2016)
-----
PCA, CA
PCA
Clear discrimination between dark & pale beer was obtained
-----
PCA
PCA
Successful discrimination was observed
(Arrieta, RodríguezMéndez, De Saja, Blanco, & Nimubona, 2010) (Novakowski, Bertotti, & Paixão, 2011)
-----
PCA
PCA
Good discrimination was observed with PC1 (82.14%) & PC2 (11.66%) variance
-----
Normalized
Autoscaling
-1.0 to 0.5 V
-0.9 to 0.8 V
Sample prepared at 0, 2, 4, 6, 8, 10, 18, & 26 days
f DFA,
-0.3 to 1.5 V
-----
oo
Average prediction accuracy 97.22%
-p r
ANN
re
Wine
DFA, PCA, ANN PCA, DFA,
lP
Wine
24
Qualitative classification rate was 100% with mid-level fusion of sensors
na
Apulian red wines
(Blanco, et al., 2015)
LDA
ur
Chinese rice wine
Jo
Rice wine
Bell-shaped windowing, Compression method Fast Fourier transform
-----
(J. Gutiérrez, MorenoBarón, Cetó, Mimendia, & del Valle, 2012) (Lu & Cai, 2014) (ZhenBo Wei, et al., 2016) (Lvova, et al., 2018)
(Díaz & Acevedo, 2014) (Cetó, et al., 2017)
(Lan, et al., 2017)
Table 5. Application of e-nose for quantitation, discrimination, and authentication of alcoholic beverages Beverage type
Parameters
Apulian wines Red wine
18 Volatile chemical compounds prediction 4-ethylphenol (µg L-1), 4-ethylguaiacol (µg L-1) Predict wine quality parameters & sensory descriptors Aging time (months)
No of sample/ division/ Quantification 17 SPE/GC-MS 46 GC-MS 28 GC-MS, Sensory panel 20×3=60
4-ethylphenol (µg L-1)
213
Rice wine
Marked age (years)
250/150:100
Liquors
25 GC-MS
Apulian wines
Characterization of famous liquors based on their flavor & origin Discrimination according to different grape variety
17
Chinese rice wine Chinese spirits
Classification of wine according to their age Classification of spirits according to their flavor
Beer
Aging fingerprint characterization for
42
Wine
Sugar cane spirits Red wine
Pretreatment
Chemometrics
Optimal model
Precision
Reference
PLS, RSR
RSR
R2=0.418-0.950, RMSEP=0.05-15
(Capone, et al., 2013)
PLS
PLS
PLS, PCA
PLS, PCA
r2=0.91, RMSECV=118.4 r2=0.89, RMSECV=20.4 Better results were observed for sensory score prediction than the quality parameters
(Berna, Trowell, Cynkar, & Cozzolino, 2008) (Jesus Lozano, et al., 2007)
Logarithmic transformation SNV
PLS
PLS
R=0.99, RMSE=0.6, r=0.98, RMSE=1.2
PLS
PLS
Normalization, Feature extraction -----
PLSR, SVM
SVM
R2cal=0.70, RMSECV=142.8 (barrel) R2cal=0.90, RMSECV=75.7 (bottle) R2=0.9942, RMSE=0.0404
(Martí, Pino, Boqué, Busto, & Guasch, 2005) (Cynkar, Cozzolino, Dambergs, Janik, & Gishen, 2007) (Zhebo Wei, Xiao, Wang, & Wang, 2017)
PCA, HCA
PCA, HCA
Higher differentiation was observed for both chemometrics
(Xiao, et al., 2014)
-----
PCA
PCA
(Capone, et al., 2013)
32
-----
PCA, DA
DA
Negroamaro & Primitivo wines were successfully classified, PC1 (67.50%), PC2(23.59%) Correct classification rate was 96.88%
144/96:48
-----
PCA, CA, SVM, LDA, BP-ANN PCA, LDA, PNN, RBF, BP
SVM
Training and testing accuracy were 91.6 & 83.3%, respectively
(M. Liu, et al., 2012)
BP
Classification accuracy for aged alcoholic beer was 90.47%
(Ghasemi-Varnamkhasti, et al., 2011)
----SNV, feature extraction Normalized
Autoscaling
(Yu, Dai, Yao, & Xiao, 2014)
Autoscaling
66 Datasets I 120 Datasets II 90 Datasets III
Vector normalization, Feature extraction
Wine
Pomegranate wine Spanish wine Red wine
Classification of five wine types Monitor spoilage induced by Brettanomyces yeast
Beers
Sauvignon blanc wines Beer, wines & spirits
SLDA
Classified correctly 86% of the samples
(Cynkar, Dambergs, Smith, & Cozzolino, 2010)
Discriminate correctly with a minimum accuracy of 97.2% Successfully discriminate the control & off-flavor doped-wines
(J Lozano, Santos, & Horrillo, 2005) (Ragazzo-Sanchez, Chalier, & Ghommidh, 2005)
Good discrimination was observed with PC1 (65%) & PC2 (19%) variance Successfully discrimination was observed between samples
(García, et al., 2006)
Autoscaling, Normalization -----
PCA, PNNs
PNNs
48
-----
PCA
PCA
PCA, PNN
PCA
PCA
PCA
PCA, CDA
CDA
PCA, CA
Centering, scaling Normalization
9
48
-----
(García-Martínez, et al., 2011)
Good discrimination between control & treated sample was observed
(Martin, Mallikarjunan, & Zoecklein, 2008)
Excellent discrimination was observed
(Cabañes, Sahgal, Bragulat, & Magan, 2009)
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20
f
9 Flavors
5×2 strain
Normalization
PCA, CA
17
Centering, scaled, feature extraction -----
PCA, ANNs, PNN
ANNs
Classification success rate was 97%
(J Lozano, Arroyo, Santos, Cabellos, & Horrillo, 2008)
PCA
PCA
Good discrimination was observed with PC1 (86.38%) & PC2 (8.30%) variance
(Lan, et al., 2017)
Normalization
PCA
PCA
Excellent discrimination was observed with PC1 (83.8%), & PC2 (7.50%) variance Correctly classified 67% of sample, further improvement needed improve the calibration accuracy & robustness. Classification accuracy was 100% in both training & test set
(Bellincontro, García-Martínez, Mencarelli, & Moreno, 2013) (Cynkar, et al., 2007)
Higher misclassification rate was observed
Sample prepared at 0, 2, 4, 6, 8, 10, 18, & 26 days 5 types
Discrimination of geographical origin Identification & classification of different alcoholic beverages Identification with different marked ages
34
Identification & discrimination of wine aroma Off-flavors detection in alcoholic beverages
16
Whiskey
Detection of methanol contamination
Wine
Classification of various kings of wines Effect of temperature on the fingerprint of wines Classification of wines by their varietal origin
1, 5, 10, 20% Methanol contamination 4 Types of wines
SLDA
PCA, LDA, SIMCA, PLSDA, SVM
LDA, SIMCA, PLS-DA, SVM LDA
LDA
-----
PCA, DFA
PCA, DFA
Allowed to clearly visualize the differences among the classes
PCA, LLE, LDA
LDA
Best identification rate was observed
(Berna, Trowell, Clifford, Cynkar, & Cozzolino, 2009) (Ragazzo-Sanchez, Chalier, Chevalier, Calderon-Santoyo, & Ghommidh, 2008) (Zhebo Wei, et al., 2017)
PCA, PNN, ANN
ANN
Success rate in classification was 100% in white wine & 98% in red wine
(Jesús Lozano, Santos, Sayago, Gutierrez, & Horrillo, 2004)
PCA, DFA
PCA, DFA
Clear visualization among brands or origins were observed
Feature extraction
PCA
PCA
Perfect classification between pure & methanol contamination
Centering, Scaling Autoscaling, Smoothing, Normalization Normalization
PCA, PNN
PCA, PNN PLS-DA
Classification success rate 100% with polysilicon heater Correct classification rate ranged between 86-100% for 60, 70, 75, 80, & 85 ˚C
(Ragazzo-Sanchez, Chalier, Chevalier-Lucia, CalderonSantoyo, & Ghommidh, 2009) (Wongchoosuk, Wisitsoraat, Tuantranont, & Kerdcharoen, 2010) (Aleixandre, et al., 2008)
DPLS
Correct classification rate >90% for both varieties
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Normalization, Feature extraction -----
40 (beer) 48 (wine)
15 white variety, 15 red variety 150
(Ghasemi-Varnamkhasti, Mohtasebi, Siadat, Ahmadi, & Razavi, 2015)
-----
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24 (beer) 72 (wine) 72 (spirit) 250
PCA, SLDA
na
Feature extraction, Autoscaling
Australian white wines
(Wongchoosuk, Choopun, Tuantranont, & Kerdcharoen, 2009) (Vera, et al., 2011)
Complete characterization of beer was produced
Beer-2 types 4-alcoholic 2-non alcoholic
Wine
(Fu, Li, Qin, & Freeman, 2007)
Fisher LDA
Discrimination of beers
Beer & wine
Average classification correction rates for Datasets I, II and III were 97.5%, 93.9% and 61.6%, respectively
PCA, Bayesian LDA, SLDA, Fisher LDA PCA, PLS-DA, SLDA
SNV
Wine
(Santos, et al., 2004)
-----
213
Rice wine
Overall classification success 88.3%
Clear discrimination was observed with PC1 & PC2 variance of 80.60 & 16.29%, respectively.
60
Wine
PCA, PNN KIII
PCA
Classification based on geographical origin (Australia, & Spain) Discrimination of added aromatic compounds Off-flavor characterization of wine using dehydration & desalcoholization step Differentiation of different red wines Differentiation of wines partially fermented by two osmo-ethanol-tolerant Yeasts Discrimination of wine with different pre-harvest & post-bloom spray treatments Discriminate of two fungal species responsible for contamination Recognition & detection of wine aging for control of fraud Differentiation of fermentation time
Wine
(Buratti, et al., 2004)
PCA
Tempranillo wine
Sweet wines
Correct classification rate 100%
-----
67
Red wine
KIII, BP-ANN, NPA
LDA
30
Discrimination of beer based on aroma profile
Wine
PCA, LDA, CART PCA, PNN
59
Beer
White wine
-----
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Wine
53
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Chinese rice wine
alcoholic beers Classify four types of barbera wines Characterization of red & white wine Discriminate six typical VOCs (ethanol, acetic acid, acetaldehyde, ethyl acetate, lactic acid and isoamyl alcohol) Discrimination of red and white wine
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Italian barbera wine Madrid wine
-----
PCA, PLS-DA
PCA, DPLS, LDA
(Cozzolino, Cynkar, Dambergs, & Smith, 2010) (Cozzolino, Smyth, Cynkar, Dambergs, & Gishen, 2005)
Table 6. Data fusion approach for quantitation, discrimination, and authentication of alcoholic beverages Beverage type
Parameters
Chinese rice wine
DPPH (mg TEAC/L) ABTS (mg TEAC/L) FRAP (mg TEAC/L) TPC (mg GAE/L)
Wine
Oxygen related parameters (mg L-1) Polyphenols (mg L-1) Colour indices (mg L-1) Original extract (%) Real extract (%) Ethanol (%) Sensorial descriptors and
Beer
No of sample/division /sensor used 111/83:28 ATR-IR, Raman
Wavelength range 4000-400 cm-1 200-2000 cm-1
16 e-nose, e-tongue
43/15:28 NIR, MIR 15
Chemometrics
Optimal model
1st Dev, 2nd Dev, MSC, SNV, Baseline, detrending
PLS, SiPLS, SiPLSC, SiSVMC
SiSVMC
-1.0 to 1.3 V (etongue)
Kernel functions
PLS1
PLS1
800-2857 nm, 4000-600 cm-1
-----
PLS, ANN
ANN
-----
Gas
Gas
-----
Pretreatment
Precision R2c=0.9387, RMSECV=5.63, R2p=0.9331, RMSEP=5.89 R2c=0.9451, RMSECV=3.52, R2p=0.9401, RMSEP=3.83 R2c=0.9686, RMSECV=11.22, R2p=0.9628, RMSEP=12.02 R2c=0.9572, RMSECV=16.59, R2p=0.9529, RMSEP=17.94 Rc>0.840, RMSECV<0.126, Rp>0.767, RMSEP<0.193 Rc>0.889, RMSECV<96.05, Rp>0.754, RMSEP<166.37 Rc>0.812, RMSECV<1.868, Rp>0.745, RMSEP<2.924 RMSEP=0.14 RMSEP=0.076 RMSEP=0.091 R2>78%
Reference
(Wu, Xu, et al., 2016)
(Rodriguez-Mendez, et al., 2014)
(Iñón, Garrigues, & de la Guardia, 2006) (Buratti, Ballabio,
Red Spanish wines
Characterization of wines
Beer
Classification of beer flavor Chemometric classification of beers
Red wine
Identification of different wine classes
(Rodríguez-Méndez, et al., 2004)
SVM, RF, ELM PCA, LDA
ELM
Prediction accuracy 98.33% achieved with fusion model
(Men, et al., 2017)
LDA
Relative higher accuracy was achieved with mid-level fusion model
(Tan, Li, & Jiang, 2015)
PCA, LDA
LDA
Correct classification rate was 96% based on the combination of potentiometric & voltammetric sensors
(J. M. Gutiérrez, et al., 2013)
PCA, SIMCA, PLS-DA
PLS-DA
Overall classification rate was 93% with NIR & MIR fusion model
(Cozzolino, et al., 2011a)
-1.0 to 1.3 V (etongue) -1.0 to 1.3 V (etongue), 780-380 nm (e-eyes)
Kernel functions
PLS-DA
PLS-DA
PCA, PLS-DA
PLS-DA
-1.0 to 1.5 V (etongue), 780 to 380 nm (optical system) -----
Autoscaling
PCA
Normalization
290-700 nm, 240-400 nm, 380–700 nm
1st Dev, Smoothing, Savitzky-Golay
-1.0 to +1.2 V
Feature extraction
400-2500 nm, 400-4000 cm-1
-----
-----
-----
Autoscaling
PCA
PCA
Clear discrimination as a function of time was achieved using sensor fusion
(Gil-Sánchez, et al., 2011)
-----
Autoscaling
PCA, PLS-DA, ICA
PLS-DA
Correct classification rate was 100%
(Di Natale, et al., 2004)
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36 e-nose, e-tongue 16 e-nose, e-tongue 16 e-nose, etongue, e-eyes 6 wines e-nose, etongue, optical system 5 beers e-nose, e-tongue 135 Fluorescence, UV & visible Spectroscopies 25 e-tongue based on multisensor 64/34:30 Vis & NIR, MIR 3 wines (1, 5, 9, 15, 19, 22, 28, 36, & days) e-nose, e-tongue 36 e-nose, Etongue
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Figures
Coupler
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Single fibers Raman Spectroscopy
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Figure 1. Configuration of the wine fermentation monitoring system with the auto-calibration Fourier transform Raman spectroscopy. Reproduced with permission (Wang, et al. 2014) Copyright 2014 Elsevier Ltd.
Signal conditioning system PB filter
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High impedance
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Figure 2. Schematic diagram of e-nose and e-tongue based measurement system. Reproduced with permission (Gil-Sánchez, et al., 2011) Copyright 2011 Elsevier Ltd.
Fusion level-based spectroscopy system Low-level
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Smartphone based CSA system Printed CSA
High-level
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Exposure to sample
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Figure 3. Block diagram of model development using smartphone based colorimetric sensor array (CSA) system, and fusion level based spectroscopic system.
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Figure 4. Selection and combination of optimum variables extracted from the Raman spectroscopy spectra and attenuated total reflectance infrared spectroscopy spectra. Adapted with permission (Wu, Xu, et al., 2016) Copyright 2015 Elsevier Ltd.
Highlights 1. Consumption of alcoholic beverages is an integral part of many socio-cultural traditions 2. Existing analytical methods of quality control do not fulfil the industrial requirements 3. Nondestructive techniques have great potential to substitute the traditional methods
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4. Integration of multiple sensors can generate meaningful results to deliver better output
S0924-2244(20)30695-6
DOI:
https://doi.org/10.1016/j.tifs.2020.11.021
Reference:
TIFS 3043
To appear in:
Trends in Food Science & Technology
Received Date: 14 May 2020 Revised Date:
20 October 2020
Accepted Date: 25 November 2020
Please cite this article as: Arslan, M., Tahir, H.E., Zareef, M., Shi, J., Rakha, A., Bilal, M., Xiaowei, H., Zhihua, L., Xiaobo, Z., Recent trends in quality control, discrimination and authentication of alcoholic beverages using nondestructive instrumental techniques, Trends in Food Science & Technology (2021), doi: https://doi.org/10.1016/j.tifs.2020.11.021. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.
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Recent trends in quality control, discrimination and authentication of alcoholic beverages
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using nondestructive instrumental techniques
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Muhammad Arslan1, Haroon Elrasheid Tahir1, Muhammad Zareef1, Jiyong Shi1, Allah
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Rakha2, Muhammad Bilal1, Huang Xiaowei1, Li Zhihua1, Zou Xiaobo*1
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School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd., 212013 Zhenjiang, Jiangsu, China.
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National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan.
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*Corresponding author. Tel: +86 511 88780174; Fax: +86 511 88780201
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Email address: [email protected] (Zou Xiaobo)
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Abstract
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Background: The consumption of alcoholic beverages is an integral part of many
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socio-cultural traditions. The existing methods of compositional analysis and quality
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control of alcoholic beverages are not satisfying the burgeoning demands of global
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market due to their high cost and slow turnaround time.
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Scope and approach: The nondestructive instrumental techniques can be applied for
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quality control, discrimination and authentication of alcoholic beverages. The cost of
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analysis, reduction in time, environment friendliness, and non-destructive nature of
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these techniques offers bright prospects for the future.
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Key findings and conclusions: This review aims to highlight the most relevant and
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current information on the use of nondestructive instrumental techniques for
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evaluating quality, discrimination and authentication of alcoholic beverages.
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Moreover, it will serve as a guidance document for large scale production units for
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implementation of these laboratory-based smart techniques. This article also covers
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the challenges faced by the beverage industry which needs to be resolved or
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investigated in future studies.
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Keywords: Alcoholic beverage; Chemometrics; Electronic panel system; Spectroscopic
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technique; Multisensor fusion.
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1. Introduction The alcoholic beverage is a drink containing ethyl alcohol or ethanol, produced by the
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fermentation of grains, fruits and other starches. The matrix of alcoholic beverages is a
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complex mixture of ethanol, water, sugars, organic acids, protein, peptides, phenolics and
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volatile aromatic compounds, all of which contribute to the typical sensory attributes of the
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drink (Cozzolino, 2012; Cynkar, Cozzolino, Dambergs, Janik, & Gishen, 2007; Penza &
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Cassano, 2004; Waterhouse, Sacks, & Jeffery, 2016). The consumption of alcoholic drinks
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plays an imperative role in the socio-cultural traditions of many societies. The global
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alcoholic beverages industry valued in excess of $1 trillion in 2018 and is expected to grow
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by 3% over next 5 years (IWSR, 2018).
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The alcoholic fermentation is a biological process involving several enzymes originating
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from diverse range of microorganisms, especially yeast. During fermentation, sugars
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(glucose, fructose, and sucrose) are transformed into cellular energy, producing carbon
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dioxide and ethyl alcohol as by-products (Cavaglia, et al., 2020; H. Li, et al., 2020). The
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heterogeneity in the composition and quality of finished product is often the result of an
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unstable fermentation. Moreover, consumers, regulators, exporters and produces are in
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demand of cost-effective analytical tools for routine quality control of alcoholic beverages
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(Acevedo, Jiménez, Maldonado, Domínguez, & Narváez, 2007; do Santos, et al., 2019;
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Okaru, et al., 2019). Apart from fermentation process, the quality of alcoholic drinks depends
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mainly on the type of raw material used, soil conditions, climate parameters and agronomic
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practices (Szambelan, Nowak, Szwengiel, & Jeleń, 2020; Veljović, Nikićević, & Nikšić,
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2019). The authenticity of alcoholic beverages is one of the most critical issue in term of
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quality and safety. The authenticity of the drinks is regulated by strict guidelines established
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by national regulatory establishments (Kamiloglu, 2019; Martins, Talhavini, Vieira, Zacca, &
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Braga, 2017). As a result, in a highly competitive environment, the industries are required to
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invest in developing appropriate technologies to improve the quality of alcoholic beverages
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while sustaining growth.
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The quality control of alcoholic drinks can be carried out using two type of methods
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namely, subjective and objective. Subjective approaches are primarily based on human
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judgement of the quality characteristics of the alcoholic beverages (Borràs, et al., 2015; Y.-b.
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Huang, Lan, & Lacey, 2004). These include perception of texture, color, odor, flavor and
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mouthfeel by the consumers or panel of experts (Polášková, Herszage, & Ebeler, 2008).
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Albeit, human assessors may be highly skilled personnel, their opinion may differ owing to 3
their psychological variability (Y.-b. Huang, et al., 2004; Smyth, 2005). The sensory
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evaluation by highly trained panel of experts is also time consuming and susceptible to large
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scale variation. Naturally, such estimation can be biased and subjected to day-to-day
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variations (Jellema, Janssen, Terpstra, de Wijk, & Smilde, 2005; Smyth, 2005). On the other
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hand, objective methods of quality control of alcoholic beverages involve different
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nondestructive instrumental techniques. These methods offer numerous advantages since they
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are reproducible, less erroneous, non-subjective, and above all, are not subject to adaptation
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or fatigue (Ross, 2009; Sáenz-Navajas, Fernández-Zurbano, & Ferreira, 2012; Tan, Li, &
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Jiang, 2015). Keeping in view the complex nature of alcoholic drinks, developing and use of
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nondestructive instrumental methods offer numerous advantages in quality control,
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geographical discrimination and authenticity. However, to be of practical use in alcoholic
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beverage industry, these methods need to be rapid, cost-effective and provide reproducible
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results with continuous operation.
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Mass spectrometry (MS) is a powerful analytical technique used to identify and quantify
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analytes using the mass-to-charge ratio (m/z) of ions generated from a sample (Rifai,
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Horvath, Wittwer, & Hoofnagle, 2018). The analyzers used in such devices range from
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simpler quadrupole time-of-flight to more complex Fourier transform mass analyzer.
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Nowadays, MS can be used in direct mode without chromatography, speeding up analysis
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times, and reducing the complexity. MS coupled with liquid chromatography (LC) or gas
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chromatography (GC) along with its characteristic high specificity and sensitivity is a popular
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choice for analysis of proteins, vitamin, lipid, traces elements, among others (Aebersold &
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Mann, 2016). However, the careful sampling and sample preparation are critical steps for
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precise and accurate measurements of desired analytes. The analyst should be well trained in
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sample collection, handling, analysis as well as appropriate record keeping. In most cases,
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samples undergo preparatory operations prior to analysis i.e. grinding, homogenization,
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filtration, distillation/extraction, concentration, drying (if the desire analyte is to be compared
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on dry-weight basis) or dilution (to decrease analyte concentration within the range of the
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instrument). Eventually, such preparatory steps may lead to loss of analyte owing to
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oxidation from exposure to air or light during grinding or homogenization, volatilization of
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compounds or binding to filtering materials. The distillation/extraction methods are often
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time consuming, require large amount of solvents, and occasionally involve high temperature
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for extraction and distillation, leading to artifactual changes to the sample (Heymann &
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Ebeler, 2016).
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The cost of instrumentation is quite high and may not be affordable for usage in small
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production facilities. Moreover, the understanding of basic principles and applications of MS
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can be critical for analysts entrusted to conform regulatory requirement. The isomers of
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compounds having same m/z may not be distinguished using MS. Furthermore, in high
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resolution MS experiments, several parameters such as capillary voltage, vaporizer
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temperature, needle temperature, and nebulizing gases need to be tuned time to time to get
149
precise results (Scigelova & Makarov, 2006). The existing traditional analytical methods used for compositional analysis and quality
151
control of alcoholic beverages are not satisfying the global market to meet the growing
152
demand of production. Moreover, the lack of skilled analyst which further intensified the
153
problem. The need for important information related to chemistry of a substance has
154
burdened the laboratories to the extent where analysis repetition is becoming difficult. The
155
factors such as minimal sample preparation, low cost of analysis, environmentally
156
friendliness and promptness are of paramount importance in the sustainable and modern
157
alcoholic beverages industries (Li-Chan, Chalmers, & Griffiths, 2010). In order to rapidly
158
respond to the changing demands of both the market and the consumer; it is of extreme
159
importance for alcoholic beverage industries to in place the qualitative and quantitative
160
means for evaluating the quality control, discrimination and authentication of alcoholic drinks
161
through objective methods (Borràs, et al., 2015; Debebe, Redi-Abshiro, & Chandravanshi,
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2017; Pontes, et al., 2006; Ragazzo-Sanchez, Chalier, Chevalier-Lucia, Calderon-Santoyo, &
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Ghommidh, 2009; Wu, Li, et al., 2015).
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During last 20 years, green innovative nondestructive instrumental techniques such as
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UV, Vis, near-infrared (NIR), mid infrared (MIR), Raman, Fluorescence and nuclear
166
magnetic resonance spectroscopy along with electronic nose (e-nose), electronic tongue (e-
167
tongue) and colorimetric sensor array have displayed great potential to provide solution to
168
aforementioned problems (Arslan, Xiaobo, Tahir, et al., 2018; Arslan, Xiaobo, Tahir, Zareef,
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et al., 2019; Arslan, Xiaobo, Xuetao, et al., 2018; Cozzolino, et al., 2008; Tahir, Xiaobo,
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Xiaowei, Jiyong, & Mariod, 2016; Tahir, et al., 2017). These techniques are getting popular
171
owing to their simplicity, speed, multiple analysis from single scan, little or no sample
172
preparation and environmentally friendly. In addition, multiple sensor fusion has made it
173
possible to integrate two or more sensors for innovative results (Arslan, Xiaobo, Tahir,
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Zareef, et al., 2019; Dong, Zhuang, Huang, & Fu, 2009; Men, et al., 2017; Tahir, et al.,
175
2017). If well developed, nondestructive instrumental techniques could be utilized to carry
176
out quality control through quantitative or qualitative means. 5
Nondestructive instrumental technologies are still being developed and the merits of their
178
application are already quite clear. However, the demerits include requirement of traditional
179
wet chemistry analysis for compositional parameters of alcoholic beverages, which demands
180
personal training and large set of measurements for model calibration. Additionally, the
181
interpretation of acquired data require complex mathematical processing (Arslan, Xiaobo,
182
Tahir, Xuetao, et al., 2019; Ozaki, McClure, & Christy, 2006; Tahir, et al., 2020; F. Xu, Yu,
183
Tesso, Dowell, & Wang, 2013). These drawbacks cannot be overlooked. Nevertheless, the
184
new developments in chemometrics and computer programs have proven handy to solve
185
some of these issues. On that account, the scope of nondestructive instrumental technologies
186
is brighter as it can fulfil the future needs by using suitable chemometric methods. Various
187
chemometric techniques have been an integral part of these technologies to perform data
188
analysis. The data acquired from these nondestructive instrumental techniques are complex
189
carrying a mixture of useful and redundant information and therefore require statistical
190
processing to draw meaningful conclusions. The use of chemometric algorithms has made it
191
possible to extract meaningful information from the data acquired from these nondestructive
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instrumental techniques.
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The aim of this scientific opinion is to provide the latest knowledge about the application
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of nondestructive instrumental techniques for evaluating compositional analysis, quality
195
control, geographical discrimination and authentication of alcoholic beverages. Focus will
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also be laid on the technical challenges encountered during the application of these
197
techniques and future perspectives.
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2. Nondestructive instrumental techniques
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2.1. Infrared spectroscopy
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The infrared region of the electromagnetic spectrum covers the wavelength from 100,000
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nm to 780 nm or and split into near infrared (NIR), mid infrared (MIR) and far infrared
202
subregions (Baeten & Dardenne, 2002; Osborne, 2006). The NIR spectrum covers the
203
wavelength range between 2500-780 nm (4000-12,500 cm-1) offering valuable information
204
related to chemical and physical characteristics of sample (Deidda, et al., 2019). In NIR
205
spectroscopy, sample absorbs specific frequencies from the light source corresponding to
206
overtones and combination bands of vibrational transitions of the molecule mainly of CH,
207
OH, CO, and NH groups (Deidda, et al., 2019; Nicolai, et al., 2007). Likewise, MIR region
208
covers the wavelength range from 25,000 to 2500 nm (400-4000 cm-1) exhibiting various
209
well defined and sharp peaks (Bureau, Cozzolino, & Clark, 2019). Typically, MIR
210
spectroscopy is applied to collect the information related to molecular composition. 6
The suitable mode for spectral data collection should be based on the optical
212
characteristics of sample. The reflectance mode is the easiest to obtain determination owing
213
to executing analysis without contact with sample beside fairly and higher light intensities.
214
Albeit, it is exposed to variation in surface characteristics. The transmission cell offers
215
advantages of reproducible and accurate spectroscopic determination. In addition, the primary
216
drawbacks of this mode often require semi preparation or destructive preparation of the
217
sample. Normally, spectral data acquired from transmission mode are considered better
218
compared to reflectance mode for analyzing internal disorder of the sample. Besides, light
219
penetration intensities of the sample are often low, making it a challenge to obtain precise
220
determination of transmission, specifically in environments with higher level of ambient
221
light. The pseudo absorbance (A) relative to standard reference material is determined A=
222
log(1/R), and A= log(1/T) for reflectance and transmittance spectra, respectively (Tahir, et
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al., 2019).
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2.2. Raman spectroscopy
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Raman spectroscopy is very similar to infrared since it provides information related to
226
fundamental chemical bonds within the sample matrix. This technique relies upon the special
227
phenomenon named as Raman scattering. In process, when the sample is exposed to incident
228
light, a small fraction of light is scattered by the sample. The scattered light differs in
229
frequencies from that of incident light. This scattered portion contains the evidences related
230
to vibrational band energies of organic matrix and are used to produce a Raman spectrum
231
(Bumbrah & Sharma, 2016; Skoog, Holler, & Crouch, 2017; Smith & Dent, 2019). Raman
232
technique also delivers evidences regarding chemical bonds within a compound as is
233
witnessed in infrared spectroscopy. Evidently, the source of laser in Raman spectroscopy
234
might cause fluorescence of molecular compounds, decreasing the sensitivity of this
235
technique by affecting the signal to noise ratio.
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Previous reports claimed that specific chemical bonds such as C=O, N-H, and C-H
237
produce specific peaks (W. E. Huang, Li, Jarvis, Goodacre, & Banwart, 2010). Thus, Raman
238
spectroscopy allows the detection of structural molecules, determination of characteristics
239
spectral patterns, type of particular chemical bonds, and the quantitative concentration of
240
particular components in organic matrix. It also allowed to conveniently study the molecules
241
(liquids and gas), biomolecules and polymers in the sample. The kinetic changes and
242
different processes in the structure and on the surfaces of these molecules can also be
243
monitored in a short period of time with Raman analytical system (Owen, Notingher, Hill,
244
Stevens, & Hench, 2006; Smith & Dent, 2019). The Raman scattering also offered another 7
advantage over analytical technique is its ability to work on samples containing water as a
246
major part such as alcoholic beverages (Numata, Iida, & Tanaka, 2011; Owen, et al., 2006).
247
Well-designed chemometric algorithms and databases are available for identification of
248
Raman spectral data, so both quantitative and qualitative outcomes can be obtained through
249
Raman technique (Dörfer, Schumacher, Tarcea, Schmitt, & Popp, 2010). In various studies,
250
Raman technique was also used in complement to infrared data (Gao, et al., 2018; Tahir, et
251
al., 2017). The configuration of wine fermentation monitoring system coupled with auto-
252
calibration Fourier transform Raman spectroscopy has been presented (Fig. 1.). In this
253
system, measurement optical probe is sealed by a transparent glass. The light emitted by
254
Raman spectroscopy proliferates along the fiber and irradiates through the transparent glass,
255
thus exiting the wine sample to produce Raman light. The measurement optical fiber collects
256
the Raman light and transmit to the light detection unit of Raman spectroscopy. The reference
257
optical path also transmits the other partial light beam and the reference standard of the
258
Raman spectrum is excited. Thereafter, the reference optical fiber transmits the Raman
259
spectrum of the reference standard to the light detection unit of the Raman spectroscopy.
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2.3. Fluorescence spectroscopy
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[Figure 1 here]
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The type of electromagnetic spectroscopy that examine sample fluorescence termed as
263
fluorescence spectroscopy. This nondestructive instrumental technique involves ultraviolet
264
beam of light, that excites the electron present in molecules and typically, but not necessarily,
265
cause them to emit visible light. At present, fluorescence components present in alcoholic
266
drink offers a great perspective and makes the fluorescence technique highly relevant for
267
alcoholic beverage industry. Generally, organic aromatic compounds emit fluorescence with
268
planar molecular skeletons and conjugated double bonds (Lakowicz, 2006). The major
269
components present in alcoholic beverage ae mostly nonfluorescent. However, the different
270
type of minor and trace beverage components that belongs to various chemical classes emit
271
fluorescence (Christensen, Nørgaard, Bro, & Engelsen, 2006). The fluorescence properties
272
are greatly dependent upon acidity, influence of solvent, environment and presence of other
273
compounds (Christensen, et al., 2006; Karoui & Blecker, 2011).
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2.4. Nuclear Magnetic Resonance spectroscopy
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Nuclear Magnetic Resonance (NMR) technique is greatly recognized as an imperative
276
tool analysis of food sample since it allows the study of both molecular dynamic and
277
chemical composition of liquid, solid or semisolid food matrices (Proietti, et al., 2017). NMR
278
technique can be valuable for food matrices characterization in term of classification or 8
identification of geographical origin, quality control and allowing the identification of
280
counterfeits. This can be attained by using one of the two methods, either untargeted or
281
targeted analysis conferring to specific application. The targeted approach permits the
282
capturing of specific markers for authenticity of tested sample (A. Sobolev, Circi, &
283
Mannina, 2016). The liquid foods such as alcoholic and non-alcoholic beverages, fully
284
solubilized foodstuff i.e. honey, and vegetable oils are very easy to analyze through NMR as
285
they do not require specific sample preparation. Thus, high resolution spectra can be obtained
286
with no or minimal pretreatment of the tested sample. The difficulties related to the presence
287
of signals originating from abundant components i.e. water in beer or wine can be resolved
288
using specific pulse sequences to suppress these signals (Proietti, et al., 2017; A. P. Sobolev,
289
et al., 2019).
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2.5. Electronic tongue
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The e-tongue is a multisensor system based on various sensor array having limited
292
selectivity and uses advanced mathematical data analysis for signal processing through
293
multivariate analysis or pattern recognition system (Vlasov, Legin, Rudnitskaya, Di Natale,
294
& D'amico, 2005). The sensor array is selective for single or group of similar analytes and
295
delivers information related to their quantity. The e-tongue instrumentation makes use of
296
electrochemical properties of tested sample. Potentiometry and voltammetry e-tongue
297
systems are based on different type of working electrodes which serve as an adequate
298
detection system and deliver acceptable response (Ciosek & Wróblewski, 2007; Vlasov, et
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al., 2005).
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The principle of potentiometric sensors is based on the measurement of potential
301
(voltage) difference on the interface among the reference electrode and working electrode.
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The selectivity and sensitivity of the sensor relies on the interface composition. The interface
303
of potentiometric sensor consists of various type of crystalline compositions, polymeric
304
membrane, and electrode materials. The combination of various membranes with different
305
sensitivity, selectivity and cross reactivity works together for potentiometric e-tongue system
306
(Riul Jr, Dantas, Miyazaki, & Oliveira Jr, 2010).
307
Likewise, e-tongue voltammetry is used in analytical chemistry owing to its simplicity,
308
versatility, robustness and sensitivity. The principle of voltammetry is based on capture of
309
current flowing between counter and working electrodes when a potential pulse is applied
310
between them. The electrochemically active compounds present in tested samples are either
311
reduced or oxidized based upon type of working electrode and range of potential applied. The
312
primary limitation of this system is the limited selectivity owing to presence of 9
313
electrochemically active compound in the sample which contribute to the measured signals
314
below the applied potential (Bard & Faulkner, 2001). The schematic diagram of e-tongue
315
based measurement system has been presented in Fig. 2. The measurement system comprises
316
of two arrays of electrodes attached to data acquisition system for the e-tongue and humid e-
317
nose. The signal conditioning system consists of filters for rejecting the 50 Hz noise and
318
amplifiers with very high impedance. The detail of the system has been reported elsewhere
319
(Gil-Sánchez, et al., 2011). [Figure 2 here]
320 321
2.6. Electronic nose The e-nose is a nondestructive instrumental technique that imitates the function of human
323
nose and comprises of specific chemical sensors array coupled with suitable pattern
324
recognition system for identification of complex or simple odors. It captures the fingerprints
325
of volatile organic compounds (VOCs) present in the headspace of alcoholic beverage sample
326
using an array of semi selective sensors (Röck, Barsan, & Weimar, 2008). Firstly, complex
327
mixture of VOCs presents in headspace interact with appropriate receptors. One odorant
328
receptor is sensible to multiple odorants and one odorant is detected by multiple odorant
329
receptors. At second stage, the signal generated by the receptors is stored in the database
330
followed by the identification of the stored signal. The output of the e-nose can be the
331
identity, concentration and characteristics of the odors as perceived by human olfaction. The
332
sensitivity for the odorant depends on the sensors attached to the sensor array of e-nose. For
333
instance, a specific odorant in the headspace of the tested sample may generate lower
334
response in one sensor or otherwise (M Ghasemi-Varnamkhasti, et al., 2011; Martı́, Busto,
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Guasch, & Boqué, 2005; Röck, et al., 2008). The schematic diagram of e-nose based
336
measurement system has been presented in Fig. 2.
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The response pattern across the sensors is very important feature for different odorant.
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This identification enables e-nose to distinguish an unidentified odorant from the sensor
339
response pattern. The test produces a unique response profile to the odorant spectrum by each
340
sensor attached to the sensor array. The response pattern generated at the sensor array is used
341
to recognize and characterize the odorant through pattern recognition methods. They are used
342
for qualitative or quantitative analysis of different VOCs in alcoholic beverages. The pattern
343
recognition method offers an advantage to characterize the complex volatile compounds
344
mixtures without identification of individual compounds (Peris & Escuder-Gilabert, 2009).
345
2.7. Colorimetric sensor array
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The practical use of human nose as an instrument for odor assessment in alcoholic
347
beverage industry is essentially limited because human olfaction is easily fatigued, subjective
348
and disordered. Thus, there is an utmost need for a nondestructive instrumental technique that
349
could mimic the human olfaction for application in alcoholic beverage industry (Xiao-wei,
350
Xiao-bo, Ji-yong, Zhi-hua, & Jie-wen, 2018). Previously, various useful applications of e-
351
nose technique have been explained but they had limited sensitivity to capture VOCs at lower
352
concentration and lacked selectivity to recognize various compounds (Buratti, Ballabio,
353
Benedetti, & Cosio, 2007; Cynkar, et al., 2007; J Lozano, Arroyo, Santos, Cabellos, &
354
Horrillo, 2008; Jesús Lozano, Santos, Sayago, Gutierrez, & Horrillo, 2004; Ragazzo-
355
Sanchez, et al., 2009; M. Xu, Wang, & Gu, 2019). The colorimetric sensory array technology
356
offered great perspectives to capture and differentiate complex VOCs. This technique mimics
357
human olfaction and produces composite response for individual odorant. The sensor array
358
produces a strong chemical interaction between active center and analyte rather than a simple
359
physical adsorption, that results in chemo responsive changes of color.
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The colorimetric sensor array technique used for chemo responsive dyes as the main
361
sensing unit and these dyes produce different color based on chemical environment. The dye
362
selection is the key step in the design of colorimetric sensor array. Usually, the dyes are
363
selected based on emission quantum yield, high absorption coefficient, large Stokes shift and
364
long emission lifetime. The choice of immobilization procedure and solid support selection
365
have greatly influenced the sensitivity, selectivity, dynamic range, stability, calibration and
366
response time. The chemo response dyes are immobilized on solid support via entrapment,
367
covalent binding, adsorption or ion exchange procedures (LaGasse, et al., 2018; Z. Li, Askim,
368
& Suslick, 2018; Zhang & Suslick, 2007). Block diagram of model development using
369
smartphone based colorimetric sensor array system has been reported in Fig. 3. The interested
370
reader can refer to a previously published review article for working principle and detailed
371
procedure (Xiao-wei, et al., 2018).
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[Figure 3 here]
372 373
2.8. Combination of Sensors
374
The utilization and advancement in multiple sensors have made available more and more
375
data for integration of two or more sensor fusion for innovative results. The multiple
376
technique involves data fusion approach from two or more sensors to acquire the optimal
377
response that otherwise would not be possible through single sensor. In data fusion the sensor
378
may be of different or same king. The various sensors have been utilized for quantitative and
379
qualitative analysis and each sensor has its own weaknesses and strengths. Thus, integration 11
380
of sensor information from multiple sensors can deliver better performance as weakness of
381
one sensor can be compensated by the strength of other (Borràs, et al., 2015; Dong, et al.,
382
2009). The schematic diagram of model development by mean of spectroscopic systems
383
based on different level of data fusion has been presented in Fig. 3. The integration of data acquired from nondestructive instrumental techniques can be
385
carried out at three levels namely low-level, mid-level and high-level fusion. Low-level
386
fusion is practically simple, built on single chemometric model and captures correlations
387
among variables of different blocks. The limitations include possible predominance of one
388
data over the other and presence of high data volume. These can be overcome by mid-level
389
fusion by extracting or selecting significant feature that allows each block to be treated
390
individually thereby reducing the dimensionality of data. Moreover, mid-level enables
391
interpretation of the results, filters block noise or background information, while, contribution
392
of each discrete block can be easily visualized. Since many preprocessing and combination of
393
feature extraction methods are possible, testing all the combinations makes the mid-level data
394
fusion computationally intensive, cumbersome and difficult to validate. Whereas, high-level
395
data fusion permits to concentrate on particularities of individual techniques. The final
396
declaration is based on fewer variables or values that are precisely embedded in the core
397
information from each technique. In this level of fusion each individual matrix is
398
independently treated and the values captured from inefficient techniques do not affect the
399
robustness of the build models as compared to other levels. Albeit, high-level demands
400
precise preprocessing of the data and the information may be lost if the correlation responses
401
between sources is not considered (L. Huang, Zhao, Chen, & Zhang, 2014; Roussel, Bellon-
402
Maurel, Roger, & Grenier, 2003). The selection and combination of optimum variables
403
extracted from the Raman spectroscopy spectra and attenuated total reflectance infrared
404
spectroscopy spectra accompanying with data fusion model development has been reported
405
(Fig. 4.). The advantage of combining two or more sensors are farfetched, making data fusion
406
very powerful and innovative analytical technique that could provide the solutions to the
407
problems where single sensor fails to accomplish it precisely.
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[Figure 4 here]
408 409
2.9. Chemometrics and data mining
410
The nondestructive instrumental techniques involving characteristic fingerprinting tend to
411
record as many features or compounds as technically possible to get a deeper insight into the
412
composition of the alcoholic beverages. The chemical bonds extending across the organic
413
matrix of alcoholic beverages vibrate at specific frequencies, that are influenced by the shape 12
414
of the molecule, period of the associated vibrational coupling, mass of the constituent atoms,
415
and stiffness of the bonds. A specific vibrational bond absorbs in the spectral region where
416
diatomic molecule exhibit only one bond that may stretch, leading to an increase or decrease
417
in distance between two atoms. However, complex molecule may have several bonds where
418
vibrations can be conjugated leading to two possible vibration modes such as bending or
419
stretching (Karoui, Downey, & Blecker, 2010; Woodcock, Downey, & O'Donnell, 2008).
420
Despite some shortcomings, specific chemical groups can be identified using absorption
421
frequencies, the feature traditionally have been regarded as a pivotal advantage of
422
nondestructive instrumental techniques. These techniques deliver unique spectrum, which
423
may be used as a fingerprint of the alcoholic beverage sample.
424
fingerprints form the basis of various applications of nondestructive instrumental techniques
425
including, discrimination of origin, compositional profiling such as polyphenols,
426
anthocyanins, aroma compounds among others, and identification of unknown contaminants
427
in the alcoholic beverages (Arroyo, et al., 2009; Borràs, et al., 2015; Capone, et al., 2013;
428
Wu, Xu, et al., 2016).
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In addition to the characteristic complexity and overlap in the fingerprint of a sample, the
430
spectrum offers greater challenge of data interpretation and quantification. However, broad
431
overlapping bands may reduce the requirement of using larger wavelength in prediction and
432
routine analysis. Recent developments in computer algorithms and instrumentation have
433
taken the advantage of this complexity, turning nondestructive instrumental techniques into
434
much more powerful and simpler tools to build reliable calibration and prediction models
435
using the unique characteristic fingerprints of alcoholic beverages (Bansal, Chhabra, Rawal,
436
& Sharma, 2014).
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The information present in fingerprints of nondestructive instrumental techniques resides
438
in the occurrence intensities, peaks, shapes and band positions. The successful approach of
439
extracting structural information, and qualitative or quantitative evidences from
440
nondestructive instrumental data is to use chemometrics or mathematical analysis (Bansal, et
441
al., 2014; Geladi, 2003; Gemperline, 2006). Thankfully, the powerful chemometric methods
442
have only been made available in recent years, but their use has become an important feature
443
for various application.
444
Since, multivariate processing is scale dependent, data from nondestructive instrumental
445
techniques are mostly preprocessed for noise reduction, and removal of systematic
446
uninformative variations. The data acquired from each technique are specifically treated
447
depending upon their explicit characteristics. Presently, many pre-processing techniques such 13
448
as data enhancement, smoothing, normalization, Savitzky-Golay, straight line, subtraction,
449
derivative transformation, scatter correction, first derivative (1st Dev), second derivative (2nd
450
Dev), standard normal variate (SNV), straight line subtraction (SLS), multiplicative scatter
451
correction (MSC), orthogonal signal correction (OSC), direct orthogonal signal correction
452
(DOSC) and iterative discrete wavelet transform (IDWT) are being used for data analysis. Currently, broad range of techniques are available for data reduction, classification and
454
regression. Principal component analysis (PCA) is commonly used tool for spectral data
455
reduction, compression and visualization. The graphical depiction can often disclose
456
clustering or pattern within a dataset since analogous samples in hyperspace are predicted to
457
discover closeness to each other. Thus, the location of unexpected sample may alert analyst
458
about outliers or unusual samples, which may be deleted from the dataset or reanalyzed prior
459
to further data processing (Arslan, Xiaobo, Shi, et al., 2020; Arslan, Xiaobo, Tahir, et al.,
460
2020; Bansal, et al., 2014). The Principal components (PCs) scores can be used for
461
mathematical modelling to classify or identify geographical origin, authentication or quality
462
control of the sample matrix. Various chemometrics algorithms available for sample
463
discrimination or authentication i.e. linear discriminant analysis (LDA), artificial neural
464
networks (ANN), partial least-squares discriminant analysis (PLS-DA), soft independent
465
modeling of class analogy (SIMCA), hierarchical cluster analysis (HCA), factorial
466
discriminant analysis (FDA), stepwise linear discriminant analysis (SLDA), probabilistic
467
neural network (PNN), radial basis functions
468
discriminate analysis (CDA), radial basis neural networks (RB-NN), support vector machine
469
(SVM), random forests (RF), extreme learning machine (ELM), locally linear embedding
470
(LLE), and canonical discriminate analysis (CDA) are used in discriminant methods (Bansal,
471
et al., 2014; Geladi, 2003; Gemperline, 2006; Shi, et al., 2017; Tahir, Xiaobo, Xiaowei, et al.,
472
2016; Zareef, et al., 2020).
(RBF), backpropagation (BP), canonical
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Likewise, it is also imperative to build prediction models of quantitative nature. Recently
474
a wide variety of approaches have been undertaken to account for quality control of alcoholic
475
beverages. Principal component regression (PCR), step multiple linear regression (SMLR),
476
partial least squares (PLS), artificial neural network (ANN), nonparametric algorithm (NPA),
477
response surface regression (RSR), chaotic neural network (KIII), synergy interval-PLS
478
(SiPLS), backward interval-PLS (BiPLS), and genetic algorithm-PLS (GAPLS) are the main
479
methods of quantitative analysis listed in the literature (Arslan, Xiaobo, Tahir, et al., 2018;
480
Arslan, Xiaobo, Tahir, Zareef, et al., 2019; Arslan, Xiaobo, Xuetao, et al., 2018; Tahir,
481
Xiaobo, Jiyong, Mariod, & Wiliam, 2016; Tahir, Xiaobo, Tinting, Jiyong, & Mariod, 2016; 14
482
Zareef, et al., 2018). The interested readers can also refer to other sources for further
483
coverage of procedures and chemometric tools (Bansal, et al., 2014; Gad, El‐Ahmady,
484
Abou‐Shoer, & Al‐Azizi, 2013; Geladi, 2003; Gemperline, 2006; Mehmood, Liland,
485
Snipen, & Sæbø, 2012; Roggo, et al., 2007; Shamsipur, Zare‐Shahabadi, Hemmateenejad, &
486
Akhond, 2006; Wold, Sjöström, & Eriksson, 2001).
487
3. Applications of nondestructive instrumental techniques
488
3.1. Near infrared spectroscopy The quality, authenticity and geographical origin discrimination of alcoholic beverages
490
could also be performed using NIR spectroscopy (Table 1). Correlation between NIR spectral
491
data and gas chromatography was performed for quantification of volatile compounds in
492
Vinho Verde wines. NIR spectroscopy data between the range of 5435-6357 cm-1 was
493
explored for further data processing. PCA and boxplot were used for outlier removal and
494
cluster identification before model development using PLS regression. The acquired results
495
for methanol, diethyl malate, ethyl octanoate, 2-methyl-1-butanol, ethyl acetate, ethyl lactate,
496
3-methyl-1-butanol, diethyl succinate, 2-phenylethanol, and 3-methylbutyl acetate were
497
considered quite good with R2 varying from 0.94 to 0.97 (Genisheva, et al., 2018). Likewise,
498
NIR spectroscopy technique integrated with GC-MS was used for determination of 1-
499
propanol, 1-butanol, and 3-methyl-1-butanol in Dukang base liquor. The combinations of
500
optimal spectral interval were captured for building the model that led to excellent results for
501
three alcohols with R2>95.21 and R2>94.72 for calibration and validation sets, respectively.
502
The RMSEP values were in the range of 0.40 to 1.35 mg/100 mL (Han, Zhang, Li, Li, & Liu,
503
2016). The UV-VIS-NIR spectroscopy was used for the determination of trans-resveratrol,
504
syringic acid, quercetin, epicatechin, catechin, malvin, and oenin in commercial red wines.
505
The prediction model was build using PLS and PCR regression. The PLS built model
506
indicated higher correlation coefficient with RMSECV value in the range of 0.14-0.88 mg L-1
507
(Martelo-Vidal & Vázquez, 2014).
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The possible quantification of different amino acids (arginine, histidine, aspartic acid,
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lysine, threonine, phenylalanine, serine, tyrosine, glutamic acid, leucine, proline, isoleucine,
510
glycine, methionine, alanine, and valine) using NIR spectroscopy has also been investigated
511
in Chinese rice wine. HPLC analysis was performed to get the reference data set. The
512
calibration statistics exhibit rcal>0.94 for all the amino acids studied except arginine, histidine
513
and proline. Similarly, cross validation results were higher than 0.81 for twelve amino acids.
514
The RPD values ranged from 1.04 to 2.77 (Shen, et al., 2010). The ability for in-line
515
determination of rice wine composition using Vis-NIR spectroscopy coupled with LS-SVM 15
was investigated. A circle light fibre spectrometer system was used to capture the spectral
517
data. The LS-SVM built model performed better compared to PLS regression yielding higher
518
rval of 0.872, 0.888 and 0.915 and lower RMSEP of 0.033, 0.146 g L-1, and 0.168% for pH,
519
titratable acidity and alcohol content, respectively (Yu, et al., 2009). The concentrations of
520
calcium, manganese, potassium, phosphorus, boron, magnesium, iron, sodium, and sulphur
521
were measured using Vis-NIR spectroscopy in Australian wines. The higher R2val 0.89, 0.86
522
and, 0.90 and lower SECV 147.6, 0.65 and, 9.80 mg L-1 were observed for potassium, iron
523
and calcium, respectively. For other minerals, intermediate values of R2val and SECV were
524
obtained (Yu, et al., 2009). The feasibility of NIR for quantitation of magnesium, potassium,
525
iron and zinc using PLSR was studied. The magnesium and potassium were well predicted
526
with Rcal of 0.885, and 0.958, respectively. The RMSEC and RMSEP ranged between 3.78 to
527
12.10 mg L-1 and 4.17 to 16.90 mg L-1, respectively (Yu, Zhou, Fu, Xie, & Ying, 2007).
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A hybrid chemometric method integrated with Vis-NIR transmission spectroscopy for
529
quantification of pH and solid content of rice wines was employed. The regression coefficient
530
and x-loading were used to propose the adequate wavelength for pH and soluble solids. The
531
acquired result reported r, RMSEP, and SEP values of 0.94, 0.02 and 0.02 for pH and 0.95,
532
0.17 and 0.16 for soluble solids, respectively (F. Liu, He, Wang, & Pan, 2007). The Vis-NIR
533
technique together with LS-SVM for nondestructive determination of pH, titratable acidity
534
and alcohol strength in colored bottles was also investigated. The prediction accuracy of LS-
535
SVM was superior to that of classical PLS with higher rval of 0.866, 0.942, and 0.960 and
536
lower RMSEP of 0.021, 0.071 g L-1, and 0.115 (%) for pH, titratable acidity and alcohol
537
strength, respectively (Yu, Niu, Ying, & Pan, 2008).
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The enological parameters in rice wine were predicted using LS-SVM and NIR
539
spectroscopy. The radial basis function and first ten PCs acquired from PCA analysis were
540
used as kernel function and input feature subset for built models. LS-SVM was slightly better
541
than that of PLS with higher R2val and lower RMSEP for pH, alcohol content, and titratable
542
acidity (Yu, Lin, et al., 2008). Vis-NIR technique was used to quantify ethanol concentration
543
in bottled rice wine. The PLS model based on original spectra yielded higher Rcal of 0.928
544
and Rval of 0.875 for ethanol prediction. The RMSEC and RMSEP were 0.135 and 0.177 (%,
545
v v-1), respectively (Ying, Yu, Pan, & Lin, 2006). The application of NIR spectroscopy for
546
determination of pH, amino acid, total acid alcoholic degree, ˚Brix and amino acids in rice
547
wine was investigated. The best results were observed for ˚Brix and alcoholic degree with
548
R2cal of 0.93 and 0.96, respectively. The RPD of alcoholic degree was recorded higher than 3,
549
indicative of robust built model. The built models for pH, amino acid and total acid were not 16
550
as good as for alcoholic degree and ˚Brix with R2cal>0.83 and RPD ranging from 1.27 to 1.41
551
(Yu, Ying, Fu, & Lu, 2006). Vis-NIR spectroscopy was used to investigate total acid, total sugar and alcohol content
553
in wine. A modified colony optimization algorithm was proposed for wavelength selection to
554
improve the accuracy of the built models. The results revealed that feature variable or
555
wavelength captured by the proposed algorithm enhanced the prediction performance and
556
robustness of the models. The R values based on ACOPLS model were greater than 0.928
557
with RMSEP ranging between 0.001 to 0.206 for all the parameters (Hu, Yin, Ma, & Liu,
558
2018). The volatile compounds, and fatty acids (decanoic acid, hexanoic acid, and octanoic
559
acid) were determined in apple wine using NIR method. The calibration resulted in full cross
560
validation (R2cv) of 0.8278 for 3,4,5-trimethyl-4-heptanol and 0.8916 for hexanol. Similarly,
561
the predictive ability (R2p) had a value of 0.8811 for 3,4,5-trimethyl-4-heptanol and 0.9184
562
for ethyl hexanoate. The RPD was greater than 2.9 in all the studied compounds. For fatty
563
acids, R2p>0.8939 with RMSECV ranging from 0.295 to 0.793 based on PLS model (Ye,
564
Gao, Li, Yuan, & Yue, 2016). Vis/SW-NIR was applied for determination of pH, alcohol
565
content and titratable acidity in sealed, colored bottles of rice wine. The LS-SVM performed
566
better as compared to PLS model with rval and RMSEP ranging between 0.866 to 0.960 and
567
0.02 to 0.11, respectively (Yu, Niu, Ying, & Pan, 2011). The effective wavelength selection
568
algorithms (SPA, RCA, and ICA) combined with Vis/NIR spectroscopy were evaluated for
569
quantification of soluble solids in beer. Ten efficient wavelengths selected by SPA achieved
570
the optimal results when combined with nonlinear model. The SPA-LS-SVM model yielded
571
excellent performance with higher r 0.9818 and lower RMSEP 0.1628 for soluble solid
572
content (F. Liu, Jiang, & He, 2009).
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In order to ensure the consistency and quality of the final product, effective monitoring of
574
fermentation is a persistent need of the alcoholic beverage industry. The pH, total reducing
575
sugars, amino acids, soluble solids, total esters, and alcohol strength are the most critical
576
parameters indicating the status of fermentation process. NIR spectroscopy as a rapid tool
577
was used to evaluate the chemical parameters during fermentation process. The SiSVM
578
model developed using combination of SiPLS and SVM was compared with classical PLS
579
model. After systematic comparison, it was observed that higher prediction accuracy was
580
observed with SiSVM built model for pH, total reducing sugars, and amino acids. The RPD
581
value for measured chemical parameters ranged from 4.67 to 11.84 (Wu, Long, et al., 2015).
582
The FT-NIR spectroscopy together with PLS regression was used for determination of pH,
583
total esters, total sugars and soluble solids in apple wine. The effective wave numbers were 17
selected using optimization function prior to model development. The optimal built model
585
yielded R2 of 0.92, 0.98, 0.93, and 0.91, and RMSEP of 0.096, 0.021, 0.077 and 0.601 for
586
total esters, total acidity, pH, and soluble solids, respectively. The RPD of all the parameters
587
was recorded higher than 3.65 (Ye, Yue, Yuan, & Li, 2014). The efficacy of NIR to monitor
588
and assess the alcohol strength and titratable acidity during apple wine making has been
589
evaluated. The spectral regions 11,995.4-7498.1 cm-1, and 6101.9-5446.2 cm-1 were selected
590
for titratable acidity and alcohol strength, respectively. The higher Rp2 and lower RMSEP
591
values in prediction set were obtained for titratable acidity (0.973, 0.21 g/L) and alcohol
592
strength (0.993, 4.25 mL/L) (Peng, Ge, Cui, & Zhao, 2016). Red wine was evaluated during
593
fermentation for alcohols (glycerol, and ethanol), sugars (glucose, and fructose) and
594
phenolics (total anthocyanins, total phenolics, and total flavonoids) using FT-NIR
595
spectroscopy. The excellent prediction model was obtained for compositional changes,
596
suggesting that FT-NIR instrumentation could be used for simultaneous and in-line
597
monitoring of red wine making process (Di Egidio, Sinelli, Giovanelli, Moles, & Casiraghi,
598
2010). NIR has also been used to quantify tannins, pigmented polymers, and malvidin-3-
599
glucoside in red wine. The sample were procured from fermentation trial conducted during
600
2001 and 2002 vintage harvest using two grape varieties. A good calibration statistics with
601
R2cal>0.80 and RPD ranging from 1.8 to 5.8 was observed for all the parameters (Cozzolino,
602
et al., 2004). Time driven changes during fermentation of Chinese rice wine were monitored
603
using FT-NIR spectroscopy. Variable selection methods and SVM were used to improve the
604
efficiency of PLS built model. The results revealed that nonlinear models performed better
605
when compared to linear model in prediction of ethanol content and total acid. The SVM
606
model based on GA variables yielded best output with R2pre of 0.94 and 0.97, RMSEP of 3.02
607
g/L and 0.10 g/L, and RPD of 8.7 and 6.1, for ethanol and total acid, respectively (Wu, Xu,
608
Wang, et al., 2015). The quality parameters of another rice wine “Makgeolli” were monitored
609
during fermentation using NIR spectroscopy. From the results, R2p was greater than 0.882
610
with SEP ranged from 0.045% to 1.233% for alcohol, titratable acidity and reducing sugar
611
(Kim & Cho, 2015).
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The discrimination of geographical origins and authenticity of alcoholic beverage was
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successfully performed using NIR technique. NIR spectroscopy coupled with PLS-DA was
614
used to classify the fermentation stages (0-3, 4-6, 7-9, & 10-20 days). The acquired results
615
revealed that fermentation stages were correctly discriminated with classification rate of
616
92.3% for calibration and 89.7% in prediction set (Wu, Long, et al., 2015). The
617
discrimination of Chinese rice wines from different geographical region was carried out. 18
618
Different wavelength ranges were selected for prediction performance comparison. NIRS
619
spectral data in the range of 1300-1650 nm gave optimal model with 100% classification of
620
wines using PLSR (Yu, et al., 2007). The fermentation stages (0, 2, 5,7 and 30-35 days) were
621
classified using FT-NIR spectroscopy integrated with LDA and algorithm SELECT. The
622
results acquired using LDA correctly classified 87% as average value from initial to final
623
phase (Di Egidio, et al., 2010). NIR spectroscopy was used to discriminate the wine age (1, 3,
624
and 5 years).
625
classification rate ranging from 93.75% to 100% (Yu, Lin, et al., 2008). Chinese rice wines
626
from different geographical region (Shaoxing, non-Shaoxing and Fujian) have been
627
discriminated using NIR spectroscopy to avoid fraudulent practices. The results reported that
628
DPLS methods successfully discriminated the samples with correct classification rate of 97.2
629
and 100%, for calibration and validation set, respectively (Shen, et al., 2012). The
630
authenticity and adulteration of Chinese liquors with and without marked age was
631
investigated using NIR spectroscopy. Higher accuracy (94.9%), sensitivity (93.1%), and
632
specificity (97.9%) were observed using SVM built model (H. Chen, Tan, Wu, Wang, & Zhu,
633
2014). Likewise, Chinese liquors sample of six different flavors, ten brands and 22 kinds
634
were discriminated using Vis/NIR spectroscopy coupled SVM, SIMCA and PCA-LDA
635
models. The results revealed that LDA model based on PCA reported better results with
636
average prediction rate of 95.70% in test set and 98.94% in training set. The correct
637
classification rate for flavor, age, alcohol percentage and brands were all greater than 95% (Z.
638
Li, et al., 2014). The combination of UV-Vis-NIR spectroscopy were also explored to
639
classify Sauvignon blanc wines from New Zeeland and Australia. Evidently, PLS-DA
640
correctly classified 86% of wines from New Zeeland and 73% from Australian regions
641
compared to that of SIMCA model. The misclassified wine samples were procured from
642
South Australia (D Cozzolino, WU Cynkar, N Shah, & PA Smith, 2011a).
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LS-SVM gave better results compared to DA analysis with correct
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NIR spectroscopy integrated with SIMCA method was applied to classify and verify
644
adulteration of whiskey, rum, brandy and vodka. The proposed method successfully applied
645
to identify the adulteration with 100% accuracy at confidence level of 95% (Pontes, et al.,
646
2006). Likewise, Shiraz wines (vintage 2006) were discriminated for their origin from
647
different Australian regions with overall success rate of 60% using NIR spectroscopy coupled
648
with LDA model (Riovanto, Cynkar, Berzaghi, & Cozzolino, 2011). Vis/NIR spectroscopy
649
was explored to discriminate between the white wines of different varietal origin (Riesling
650
and Chardonnay). The DPLS regression model correctly classified 96% of Chardonnay wines
651
and 100% of Riesling compared to PCR model (Cozzolino, Smyth, & Gishen, 2003). 19
Similarly, successful classification rate of 100% and 84% of the Australian and Spanish
653
Tempranillo wines of different geographical origin was observed using Vis/NIR spectroscopy
654
combined with PLS-DA model (L. Liu, Cozzolino, Cynkar, Gishen, & Colby, 2006). The
655
successful characterization of five aging categories of beer was achieved using NIR
656
spectroscopy. Computational tools such as Fisher weights, PCA, LDA, StepLDA, KNN and
657
GA were used in this study. LDA model based on ten variables chosen by means of SELECT
658
performed better with correct prediction range of 62-86% among different categories
659
(Ghasemi-Varnamkhasti & Forina, 2014). Vis/NIR spectroscopy was employed to classify
660
commercial Riesling wines from France, New Zeeland, Germany and Australia. The results
661
reported that SLDA calibration models correctly classified 86%, 67%, 67% and 87.5% of the
662
Australian, New Zealand, French and German Riesling wines, respectively (L. Liu, et al.,
663
2008). Discrimination of wine within a controlled designation of origin was also performed.
664
Resultantly, Rosal wines were 100% correctly classified via LDA built model and accurate
665
classification (100%) was also achieved for Condado Salnés and Ribeira de Ulla wines
666
(Martelo‐Vidal, Domínguez‐Agis, & Vázquez, 2013). In conclusion, NIR spectroscopy as a
667
rapid and nondestructive tool coupled with chemometric methods could be used for
668
discrimination of origin, ascertain the authenticity, monitor compositional changes during
669
fermentation and quality control of alcoholic beverages at the industrial level.
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[Table 1 here]
3.2. Mid infrared spectroscopy
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Numerous studies have demonstrated that MIR spectroscopy can predict the quality of the
673
drinks, rapidly evaluate the geographical origin and analyze the counterfeit samples of
674
alcoholic beverages (Table 2). Previously, FTIR spectroscopy was explored to analyze the
675
parameters related to phenolic contents of wines. The results revealed that Rp2 of TPI280,
676
Folin-Ciocalteu index, Glories color parameters, and CIELab color parameters were higher
677
than 0.8538 with RMSEP value of less than 2.81, indicative of robust built models (Garcia-
678
Hernandez, Salvo-Comino, Martin-Pedrosa, Garcia-Cabezon, & Rodriguez-Mendez, 2020).
679
Another study attempted to predict total acidity, sugar content and alcohol degree in Italian
680
straw wines through MIR technique. The spectral data correlated with HPLC analysis yielded
681
R2pred greater than 0.92 for all the parameters. The RMSEP ranged from 0.029 g/L to 9.9 g/L,
682
exhibiting suitable strategy for quick and robust assessment of quality parameters of straw
683
wine (Croce, et al., 2020). Likewise, FT-MIR was successfully used to determine the
684
antioxidant capacity and bioactive compounds in Cabernet Sauvignon wines. The PLS1 20
model was indicative of Rv2, and Rc2 ranging from 0.9301 to 0.9502, and 0.9389 to 0.9480,
686
respectively for total phenolic, anthocyanins, tannins, flavonoids, ABTS and DPPH assays
687
(Grijalva-Verdugo, Hernández-Martínez, Meza-Márquez, Gallardo-Velázquez, & Osorio-
688
Revilla, 2018). The ethanol was measured in Ethiopian traditional alcoholic beverages using
689
FT-MIR spectroscopy. The PLS built model reported R2 of 0.999 with 0.1% RMSEP for
690
prediction set indicating robust built model (Debebe, et al., 2017). Similarly, grape derived
691
spirit was analyzed for quality via FTIR-ATR spectroscopy. The PLS built model yielded r2
692
in the range of 97.1-99.4 for methanol, acetaldehyde, fusel alcohol, ethyl acetate, and
693
alcoholic strength. The lower and higher RMSECV was recorded for alcoholic strength
694
(0.37%) and methanol (32.4 g/hL), respectively. The RPD was recorded higher than 2.9 for
695
all the quality parameters (Anjos, Santos, Estevinho, & Caldeira, 2016). A novel analytical
696
method for chloride and sulfate determination in wine was developed using FTIR
697
spectroscopy. The developed PLS model produced results with higher accuracy especially for
698
sulfate with R2p 0.98, RMSEP 0.11 g/L, and RPD 6.3. Similarly, built model for chlorides
699
with enough precision and accuracy yielded R2p 0.83, RMSEP 0.18 g/L, and RPD 2.4 to
700
allow semi quantitative determination (dos Santos, Páscoa, Porto, Cerdeira, & Lopes, 2016).
701
MIR spectroscopy was also explored to predict quality parameters (total phenols,
702
anthocyanin, brix, titratable acidity, pH, color intensity, sugars, and electrical conductivity) in
703
two types of alcoholic beverages, raki and wine. From the results, it was observed that
704
developed PLS model yielded R2cal ranging from 0.71 to 0.99, and R2val from 0.54 to 0.98 for
705
all the parameters. In addition, RMSEP and RMSEC varied between 0.02% to 464.70 ppm,
706
and 0.02% to 523.60 ppm, respectively (Ozturk, Yucesoy, & Ozen, 2012). A comparative
707
study was performed to analyze the alcohol degree, total sugars, non-sugar solids, total acids
708
and pH using NIR and MIR spectroscopy in Chinese rice wine. The results indicated that the
709
performance of MIR spectroscopy was better than that of NIR for total acids (R2v 0.854, and
710
RPD 2.6), alcohol degree (R2v 0.942, and RPD 4.3), non-sugar solids (R2v 0.910, and RPD
711
3.2) and total sugars (R2v 0.974, and RPD 6.0). Besides, NIR was little superior for pH
712
determination with R2v of 0.797 and RPD of 2.3 (Shen, Wu, Wei, Liu, & Tang, 2017). A fast
713
and simple method employed MIR spectroscopy for quantification of specific gravity, ethanol
714
percentage, pH, glucose plus fructose, titratable acidity and volatile acidity in commercial
715
Australian white and red wine. Results from this study demonstrated that PLS built model
716
reported R ranging from 0.65 to 0.99 with SEP recorded between 0.0007-1.35 g/L (D
717
Cozzolino, W Cynkar, N Shah, & P Smith, 2011b). Use of FT-MIR spectroscopy for fast
718
evaluation of sugars and acids was also reported in Chinese rice wine. During calibration,
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various quality parameters were precisely determined with rcal ranging from 0.821 to 0.991.
720
Besides, validation models with rval greater than 0.85 were recorded for most of the
721
parameters. The unsatisfactory prediction was observed for isomaltose and isomaltotriose.
722
The RPD captured was close to or higher than 2.0 for all the studied parameters except
723
isomaltotriose and isomaltose (Shen, Ying, Li, Zheng, & Hu, 2011). Ethanol, sucrose, and
724
tartaric acid was predicted in alcoholic beverages (vodka, gin, rum etc.) using MIR
725
spectroscopy. The built model exhibited good precision and accuracy with higher correlation
726
coefficient and lower RMSEP for all the parameters (Nagarajan, Gupta, Mehrotra, & Bajaj,
727
2006). A new approach was explored using MIR spectroscopy for determination of the
728
attenuation limit of beer samples in combination with PLS. A specific spectral region
729
between 800 and 1200 cm-1 was captured containing information related to limit of
730
attenuation. The model yielded rcal 0.979 with RMSEC of 0.40% for attenuation limit
731
(Castritius, Geier, Jochims, Stahl, & Harms, 2012). FTIR spectral data was explored to
732
evaluate the quality of beer and spirit drinks. For validation set, better accuracy and strong
733
correlation for spirits and beer parameters were observed with R2 falling between 0.90-0.98,
734
and 0.97-0.98, respectively. In addition, parameters like EBC color, bitterness and pH
735
showed lower accuracy and weak correlation, but could be determined semi quantitatively
736
(Lachenmeier, 2007). The amino acids in Chinese rice wine were investigated using FT-IR
737
spectroscopy as a novel and rapid analytical technique. The achieved results indicated that
738
prediction accuracy and precision of GA model based on spectral interval selected by SiPLS
739
outperformed with R2 higher than 0.80 and RPD greater than 2.0, compared to that of PLS
740
model (Wu, Xu, Long, Wang, et al., 2015b). In another study, FT-MIR spectroscopy was
741
successfully applied for prediction of both volatile and total acidity of red wines using IPW-
742
PLS built model based on selected spectral regions (Pizarro, González-Sáiz, Esteban-Díez, &
743
Orio, 2011).
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Monitoring fermentation parameters is the key point in controlling the final quality of
745
alcoholic beverage. Previously, ATR-MIR spectroscopy has been explored to determine the
746
total sugars, ethanol, total acids, and amino nitrogen in Chinese rice wine. The spectral data
747
was combined with efficient algorithms (PLS, iPLS, SVM, and iSVM) to explore the optimal
748
model. From the results, iSVM exhibited better results with R2 higher than 0.93 for prediction
749
set and RMSEP value ranging from 0.02 g/L to 2.35 g/L, revealing an excellent built model
750
(Wu, Xu, Long, Zhang, et al., 2015). In another study, fermentation parameters were
751
monitored using FT-IR spectroscopy in Chinese rice wine. The SiSVM model based on
752
spectral data selected by SiPLS was excellent yielding R2 >0.9311 for calibration set and 22
>0.9195 for prediction set. The RMSEP ranged from 0.03 g L-1 to 1.68 g L-1. The highest
754
RPD value was recorded for total sugars (14.60) and lowest for amino nitrogen (5.36),
755
indicating robustness of built models (Wu, Long, et al., 2015). The concentrations of alcohols
756
(glycerol and ethanol), sugars (glucose and fructose) and phenolic compounds (total
757
anthocyanins, total phenolics, and total flavonoids) were monitored during fermentation in
758
red wine using FT-IR spectroscopy. The HPLC and spectrophotometer data was correlated
759
with spectral information acquired during fermentation process to build reliable model. From
760
the results it was observed that PLS model yielded rcal in the range of 0.96-0.99 with excellent
761
cross validation models for all the parameters (Di Egidio, et al., 2010). The fermentation
762
process of Korean traditional wine “Makgeolli” was monitored rapidly and non-destructively
763
using FT-MIR spectroscopy. The built PLS model reported R2 values of 0.984, 0.983, and
764
0.936 for alcohol, reducing sugars, and titratable acidity, respectively. The SEP were
765
recorded between 0.026 to 0.595% (Kim, et al., 2016). Quantification of phenolic compounds
766
during red winemaking process was also explored using FT-MIR technique. The spectral
767
region selected was between 979 and 2989 cm-1 for each developed model. Good values of
768
performance parameters were recorded with R2cal >0.82 and R2val>0.72. The RPD recorded
769
was between 1.9 to 5.5 based on selected spectral regions (Fragoso, Acena, Guasch, Mestres,
770
& Busto, 2011). An ultra-fast, easy to use and robust method was developed for total
771
phenolic compounds and total antioxidant activity using FTIR spectroscopy during
772
winemaking. It was observed that concentration of these compounds varied at each
773
production stage. The R2cal for calibration model was greater than 0.85 for antioxidant
774
activity and higher than 0.93 for phenolic compounds (Preserova, Ranc, Milde, Kubistova, &
775
Stavek, 2015).
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Previously, wines have been discriminated for their phenolic content using PCA analysis,
777
with first three components accounting for 99.8% of total variance between samples (Garcia-
778
Hernandez, et al., 2020). The fermentation stages (0-3, 4-6, 7-9, & 10-20 days) of Chinese
779
rice wine have also been successfully classified using FT-IR spectroscopy. High
780
classification rate in both calibration set (94%) and prediction set (94.9%) was recorded with
781
PLS-DA built model (Wu, Long, et al., 2015). Likewise, FT-IR spectroscopy was used to
782
monitor the fermentation process (0, 2, 5, 7 and 30–35 days) in red wine. The LDA built
783
model outperformed in correctly discriminating the fermentation process with 100%
784
classification rate in both calibration and prediction sets (Di Egidio, et al., 2010). In India, the
785
discrimination for geographical origin of illicit liquors has been attempted through ATR-
786
FTIR spectroscopy, yielding 76% and 93% accuracy with PCA and LDA models (Yadav & 23
Sharma, 2019). The samples from Fatehgarh sahib, Pathankot, Patiala, Masha and Delhi were
788
reported with 100% classification rate, whereas 75% correct classification rate was recorded
789
in samples from Gurdaspur and Ferozpur. A classification model was also developed for the
790
identification of different types of beers (commercial vs craft beer, ale vs lager etc.). Correct
791
classification rate of 100% was reported in differentiating lager vs ale and craft beer vs
792
commercial sample. The reported results further revealed that dissolved gasses did not
793
interfere in the analysis as overlapping artefacts (Gordon, et al., 2018). Analytical approach
794
was developed for authentication and discrimination of whiskies procured from USA, Ireland
795
and Scotland. The parameters such as time of maturation (2, 3, 6, and 12) and origin were
796
considered for discrimination. The whiskey samples from USA were correctly classified in
797
all the built models, indicative of considerable chemical differences compared to that of
798
Ireland or Scotland whiskey. The unknow samples of whiskey could be identified using the
799
developed approach (Sujka & Koczoń, 2018). A comparative study was performed between
800
NIR and MIR spectroscopy for discrimination of Shiraz wines procured from Australian
801
region. The results revealed better accuracy of MIR for separation of Coonawarra and
802
Western Australia wines. It was also reported that LDA and SIMCA models based on MIR
803
achieved 73% and 95.3% overall correct classification, respectively (Riovanto, et al., 2011).
804
The South African young cultivar wines were discriminated using FTMIR and GC-MS data
805
for volatile compounds. The correct classification rate of 98.3% was achieved through MIR
806
spectral data for red cultivar, whereas combination of volatile compounds and MIR spectra
807
correctly classified 86.8% of white cultivar (Louw, et al., 2009). In another study, analytical
808
tool was developed to capture the counterfeit Scotch whisky samples using MIR
809
spectroscopy. The analysis was carried out to investigate dried residues of whisky and
810
caramel solution via ATR diamond crystal. The PCA analysis correctly classified caramel
811
solutions and whisky samples as either authentic or counterfeit (McIntyre, Bilyk, Nordon,
812
Colquhoun, & Littlejohn, 2011). Similarly, effective strategy for detection of fake liquors was
813
developed using LS-SVM built model with a prediction accuracy >97% (D. Chen, Tan,
814
Huang, Lv, & Li, 2019). The discrimination between Cognacs and other distilled beverages
815
was also achieved with PLS-DA with correct classification rate of 95% in test set (Picque, et
816
al., 2006). A rapid tool was developed for discrimination of organic and non-organic wines
817
sourced from different production systems of Australia. The success of classification was
818
100% and 88% for organic and non-organic production system in white wines, respectively,
819
whereas for red wines the success of classification was 73% for organic and 85% of non-
820
organic (Cozzolino, Holdstock, Dambergs, Cynkar, & Smith, 2009). Conclusively, MIR
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spectroscopy can be used as a rapid tool for quality control, discrimination of geographical
822
region, and identification of counterfeit samples of alcoholic beverages. [Table 2 here]
823 824
3.3. Raman spectroscopy Recently, Raman spectroscopy offers a great capability to achieve quality control in
826
alcoholic beverage industry. The major advantage of Raman spectroscopy is its ability to
827
work perfectly with water containing samples compared to other vibrational spectroscopic
828
techniques. The water in alcoholic beverages has a minute effect on scattering and does not
829
interfere with Raman scattering. The literature survey related to Raman spectroscopy is
830
presented in Table 3. Evidently, Raman spectroscopy is being employed in quality assurance
831
and control investigation of Chinese rice wine. The results demonstrate that prediction
832
accuracy of built models based on spectral intervals selected by SiPLS was greatly improved
833
as evident from R2 greater than 0.90, and RMSEP ranging between 0.05 to 2.40 g/L for
834
ethanol, pH, total sugar and acid content. The RPD recorded was higher than 3 for all the
835
parameters. (Wu, Long, et al., 2016). Similarly, combinations of linear and nonlinear
836
algorithms were explored to investigate the fermentation parameters (ethanol, and glucose) of
837
Chinese rice wine. The results explicated that prediction accuracy of built model based on
838
variables captured by CARS was expressively enhanced. Similarly, nonlinear developed
839
models outperformed in prediction of fermentation parameters. Besides, systematic
840
comparison and discussion, CARS-SVM build model delivered optimal performance in
841
predicting ethanol and glucose concentration (Wu, Xu, Long, Wang, et al., 2015a). In another
842
work, Raman spectroscopy was evaluated and compared with NIR an MIR spectroscopy for
843
assessment of alcohol strength, total acidity, density, total sugars, pH, and volatile acidity in
844
white wines. Results revealed better performance of Raman spectroscopy for determination
845
of pH and total sugar. MIR spectroscopy was considered to be most suitable for total acidity
846
and alcohol strength. The three techniques presented similar result for density, while, none of
847
them presented accurate determination of volatile acidity of white wines (dos Santos, et al.,
848
2018). While, a robust model was developed for ethanol concentration in Chinese rice wine.
849
The achieved results presented higher correlation coefficient and lower error of prediction
850
using dispersive Raman spectroscopy (Yang & Ying, 2011). In another scientific study,
851
ethanol and glucose concentration were monitored as a critical quality control parameter
852
during wine fermentation. The proposed method successfully predicted both parameters and
853
offered great prospect to be used for in-line monitoring of different fermentation parameters
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(Q. Wang, Li, Ma, & Si, 2015). A new method was developed for determination of phenolic
855
compounds in Cabernet Sauvignon red wines. The Raman scattering spectra was captured for
856
red wines corresponding to different vineyards, seasons, and harvests. The high correlation
857
was observed for polyphenols, tannins, and anthocyanins, indicating the potential of Raman
858
spectroscopy for field measurements (Gallego, Guesalaga, Bordeu, & González, 2010). The
859
novel technique was investigated for monitoring time lapse changes during wine
860
fermentation. The auto calibration system comprising of measurement optical path and
861
reference optical path was used to eliminate the intensity shift and frequency shift of Raman
862
spectra. The experimental results presented excellent prediction accuracy for ethanol,
863
glycerol, and sugar content using PCA-PLS model (Q. Wang, Li, Ma, & Liang, 2014). The
864
Raman spectroscopy was also successfully used to predict the polyphenol content and density
865
of apple liqueurs using PLS-1 model (Śliwińska, et al., 2016).
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The discrimination of Chinese rice wine at fermentation stages was performed using
867
Raman spectroscopy. The achieved results reported good performance with an average
868
correct classification rate of 94.9% based on DPLS build model (Wu, Xu, Long, Wang, et al.,
869
2015a). The Chinese rice wines belonging to different brands were also differentiated with
870
100% correct classification in prediction set using LDA model (Wu, Long, et al., 2016). In
871
another study, wine discrimination was carried out with respect to vintage, variety, and
872
geographical origin. The classification rate of 100% for both initial and cross-validation was
873
achieved for variety and geographical origin, while for vintage discrimination, 100% in initial
874
and 94.1% in cross-validation was reached (Magdas, Guyon, Feher, & Pinzaru, 2018). The
875
species of Leuconostoc, Oenococcus, Pediococcus, and Lactobacillus play an imperative role
876
in winemaking process, either as contaminants or inoculants. The metabolites of these lactic
877
acid bacteria greatly influence aroma, flavor and texture of wine. Thus, a robust model for
878
discrimination of lactic acid bacteria with a sensitivity ranging from 84% to 90% was
879
proposed using Raman spectral fingerprints (Rodriguez, Thornton, & Thornton, 2017). The
880
discrimination method was developed to identify counterfeit and adulterated samples in
881
single malt scotch whisky using Raman spectroscopy. The results explicated that PCA
882
discriminated mainly cask type, in which whisky was aged. Whereas, PLS regression enabled
883
the investigation of alcohol content, age, type of filtration process used and presence of
884
artificial color in whisky (Kiefer & Cromwell, 2017). The Raman spectral fingerprints were
885
also successfully used to distinguish silver tequila from aged tequilas (Frausto-Reyes,
886
Medina-Gutiérrez, Sato-Berrú, & Sahagún, 2005). Similarly, a fast method has been proposed
887
for discrimination among mezcal samples with variable aging time using Raman spectral data
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888
integrated with PCA and PLS-DA model (Elías, et al., 2015). In conclusion, Raman
889
spectroscopy offers great capability to solve problems related to quality control, safety
890
monitoring, discrimination and authentication of alcoholic beverages. [Table 3 here]
891 892
3.4. Fluorescence spectroscopy In recent decades, remarkable growth has been observed in the application of
894
fluorescence spectroscopy. Multiple components can be determined simultaneously within
895
few minutes with improved sensitivity and selectivity. Fluorescence spectroscopy as a
896
nondestructive, accurate, and reliable technique has offered great potential in quality control,
897
adulteration monitoring and geographical identification of alcoholic beverages. Previously,
898
brandies and mixed wine spirits have been successfully discriminated by mean of
899
synchronous fluorescence spectra tandem with backward LDA model (Uríčková, Sádecká, &
900
Májek, 2013). Another scientific opinion has followed the suite by differentiating whisky,
901
brandy, juniper and slivovice drinks using synchronous fluorescence spectra (Tóthová, 2008).
902
Similarly, front face fluorescence spectroscopy was explored to differentiate counterfeit
903
brandies from wine distillates. The synchronous application of fluorescence spectra (200-700
904
nm) and emission spectra (360-650 nm) resulted in good discrimination among the two spirit
905
classes through PCA and HCA. The higher rate of misclassification was observed using
906
excitation spectra between 225 to 425 nm at an emission wavelength of 440 nm (Sádecká,
907
Tóthová, & Májek, 2009). The front-face fluorescence spectroscopy was also exploited for
908
discrimination of wines according to typicality, variety, and vintage. The shape of emission
909
and excitation spectra changed with the wine samples. The excitation spectra were indicative
910
of good discrimination between German and French wines using PCA. Similarly, emission
911
fluorescence data set led to 95% correct classification of typical and non-typical Beaujolais
912
(Dufour, Letort, Laguet, Lebecque, & Serra, 2006). The modeling of emission-excitation
913
matrices to classify Argentine white wines according to the grape varieties has also been
914
carried out. The PCA score plot presented strong overlapping between different classes. The
915
results yielded U-PLS-DA and SPA-LDA as an efficient models for discrimination of white
916
wines with an accuracy of ca. 90% (Azcarate, et al., 2015). A simpler method for
917
discrimination of fruit spirits using synchronous fluorescence spectroscopy has been
918
employed previously. The general DA model led to 100% classification rate compared to that
919
of LDA model for fruit spirits (Tomková, Sádecká, & Hroboňová, 2015). Polyphenols
920
composition of wines is governed by the variety of grape used during the process. The
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921
fluorescence spectral data acquired at neutral pH are not always sufficient to differentiate the
922
wines. The pH modification results in structural changes in polyphenols leading to significant
923
alterations in their spectra, making it possible to more precisely differentiate wine samples.
924
Independent component analysis captured the informative signals from the matrix and
925
showed significant improvement in discrimination of wines with pH modification (Saad,
926
Bouveresse, Locquet, & Rutledge, 2016). The addition of ethanol or water is a most common mean to adulterate brandy. To avoid
928
misleading of consumers, excitation-emission matrix fluorescence coupled with parallel
929
factor analysis and PLS regression was used to quantify the concentration of methanol,
930
ethanol, and water in adulterated brandy. The developed models were able to predict studied
931
parameters with coefficients of determination greater than 0.993, and RMSEP ranging from
932
0.20% to 0.24% (Markechová, Májek, Kleinová, & Sádecká, 2014). In another study, brandy
933
adulteration with mixed wine spirit was determined by mean of fluorescence spectroscopy.
934
The model created using parallel factor analysis was capable of predicting different levels of
935
mixed wine spirit in adulterated brandy with higher correlation coefficient (0.995) and lower
936
RMSEP (1.9%) (Markechová, Májek, & Sádecká, 2014). Similarly, fluorescence
937
spectroscopy was used to determine gallic, vanillic, syringic and ferulic acids along with
938
scopoletin in brandy using PLS2 model. The developed method was able to predict chemical
939
compounds with higher correlation coefficient and lower SEP values. The reported results
940
were comparable to those acquired by means of HPLC reference method (Žiak, Sádecká,
941
Májek, & Hroboňová, 2014). Evidently, these tools could be helpful for discrimination,
942
quality control, and adulteration assessment of alcoholic beverages in mass production units.
943
3.5. Nuclear magnetic resonance spectroscopy
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The NMR spectroscopy is becoming more and more popular for testing of alcoholic
945
beverages. A single NMR measurement can answer numerous quality related issues that
946
could not be resolved through traditional methods. A number of scientific opinions have
947
highlighted the use of NMR technique for identification, quality control, and adulteration
948
detection of alcoholic beverages. Previously, characterization of wines produced from grape
949
variety “Greco bianco”, in different Italian regions, i.e. Campania and Calabria were achieved
950
by means of NMR and conventional analysis. The results achieved significant discrimination
951
using PCA and PLD-DA method. In particular, PLD-DA allowed the selection of compounds
952
(glucose, proline/arginine ratio, total acidity, and total polyphenol index) that were useful for
953
discrimination of wines based on their geographical origin (Caruso, et al., 2012). In a study
954
involving beers, NMR spectral data tandem with PCA demonstrated promising potential in 28
detection of compositional differences attributed to time and site differences (Almeida, et al.,
956
2006). Similarly, NMR spectroscopy was employed to differentiate Brazilian lager beers of
957
two classes, varying in style and information available on the label (da Silva, Flumignan,
958
Tininis, Pezza, & Pezza, 2019). The acquired results reveled higher rate of discrimination
959
through SIMCA and PLS-DA model. In another scientific opinion, NMR spectroscopy
960
showed that lagers and ales beers differ primarily in their aromatic compositions, thus can be
961
discriminated on a PCA scores plot (I. Duarte, et al., 2002). NMR spectroscopy was also
962
explored for quality control of beers differing in label and types. In particular, most lagers,
963
ales and alcohol-free sample were discriminated by means of their aromatic compositions,
964
reflecting higher sensitivity of NMR regions towards different fermentation processes (I. F.
965
Duarte, Barros, Almeida, Spraul, & Gil, 2004). NMR spectral profiles have been fused with
966
stable isotope (SNIF-NMR,
967
fusion model yielded improvement up to 100%, compared to that of single NMR data (82-
968
85%) for prediction of geographical origin. Though some improvement was observed for
969
vintage year (99% for fusion model), however, detection of varietal differences of grape
970
stayed stagnant (Monakhova, et al., 2014). In another study, genotoxic carcinogen (ethyl
971
carbamate) was analyzed by mean of NMR spectroscopy in stone fruit spirits. The results
972
presented reliable quantification of ethyl carbamate below the target level (1 mg/L)
973
recommended by European Commission (Monakhova, Kuballa, & Lachenmeier, 2012).
974
Similarly, NMR spectroscopy was explored for authenticity and quality control of beers.
975
NMR spectra correlated with wet chemical analysis and offered great capability to predict
976
lactic acid, ethanol and gravity. The significant discrimination was observed for beers with
977
deterioration in quality. The beer made from wheat malt could also be differentiated from
978
those of barley malt. The clustering of beers from the same brewing sites was also noticed
979
(Lachenmeier, et al., 2005). These observations highlight the importance of NMR
980
spectroscopy as a powerful tool in quality control, identification of geographical origin and
981
authenticity assessment of alcoholic beverages.
982
3.6. Electronic tongue
13
ro
O,
O) to enhance the classification accuracy of wines. The
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The e-tongue sensor array integrates the concept of global selectivity to distinguish very
984
similar liquids, where the changes in electrical response of various materials act as a
985
fingerprint for the investigated sample. The captured fingerprints are mathematically
986
processed to make them meaningful. The applications of e-tongue system for quantitation,
987
discrimination, and authentication of alcoholic beverages has been summarized in Table 4.
988
The parameters related to phenolic compounds were investigated using e-tongue system in 29
red wines. The eight type of Spanish wines varying in age, grape varieties and geographical
990
regions were analyzed. Th PLS regression was used to develop prediction models with higher
991
correlation (Rp2 >0.8944) and lower error for prediction (RMSEp<2.60) for parameters related
992
to phenolic content of red wines (Garcia-Hernandez, et al., 2020). The potentiometric e-
993
tongue technique was also successfully used to quantify ferulic acid, sinapic acid and gallic
994
acid with correlation coefficient higher than 0.976 in beers (Cetó & del Valle, 2014). The
995
training model for alcohol content in beer was developed with r and RMSE of 0.995 and
996
0.21%, respectively (Cetó, Gutiérrez-Capitán, Calvo, & del Valle, 2013). The portable e-
997
tongue system based on an array of electrochemical screen-printed electrodes was explored
998
for investigation of alcohol strength and color index. The developed multivariate model was
999
able to predict alcohol strength and color index with an accuracy of 86% and 76%,
1000
respectively (Blanco, De la Fuente, Caballero, & Rodríguez-Méndez, 2015). Apart, the
1001
voltammetric e-tongue system was used to predict the total sugars and dry extract of cava
1002
wines with different ageing time. The quantitative models employing ANNs revealed higher
1003
accuracy and precision for investigated parameters (Cetó, Capdevila, Puig‐Pujol, & del
1004
Valle, 2014). The prediction model for different marked ages was established based on
1005
voltammetric e-tongue system. The regression results presented higher correlation for
1006
Chinese rice wine with different marked ages using PLS and BP-ANN model (Zhenbo Wei,
1007
Wang, & Ye, 2011). The taste sensing system was employed to predict the flavor and marked
1008
age in rice wine. The SVM built model relying on leave-one-out cross validation showed
1009
more accuracy and stability than that of PLS regression model with prediction correlations of
1010
0.9620, and 0.9568 for flavor and marked age, respectively (ZhenBo Wei, Wang, Cui, &
1011
Wang, 2016). The ELM built model successfully predicted the geographical origin of rice
1012
wines with R2, and RMSEC of 0.9436, and 0.36 in testing set, respectively (J. Wang, Zhu,
1013
Zhang, & Wei, 2019). The aging time and global sensory score of wine was also predicted
1014
with e-tongue system. The PLS model yielded r values of 0.969, and 0.917, with NRMSE
1015
0.077, and 0.110 in the prediction set, for aging time and sensory scores, respectively (Cetó,
1016
et al., 2017). The hybrid e-tongue based on voltammetric and potentiometric sensors was
1017
further assessed for process duration, ethyl alcohol content, density, extract content, and
1018
reducing sugars (Kutyła‐Olesiuk, Wesoły, & Wróblewski, 2018). The results indicated that
1019
hybrid system outperformed with excellent correlation coefficient for quantitative assessment
1020
of wine production. In another study, the capability of voltammetric e-tongue to analyze the
1021
phenolic content and density of apple liquors (Nalewka) was compared with spectroscopy
1022
techniques (Raman, and UV-Vis spectroscopy). The PLS-1 model based on e-tongue system
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989
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yielded better or nearly similar correlation for aforementioned parameters in calibration and
1024
validation sets, compared to that of spectroscopy technique (Śliwińska, et al., 2016).
1025
Similarly, E-tongue technique based on 18 potentiometric chemical sensors was applied for
1026
quantitative investigation of beer. A good correlation was observed between sensors and
1027
physicochemical variables (bitterness, polyphenols, alcohol volume, and real extract) with
1028
RMSEP in the range of 0.60 Plato˚ to 52.00 mg/L (Polshin, et al., 2010). The sugars, organic
1029
acids, amino acids, and sensory score were predicted in Korean rice wines using e-tongue
1030
system. The developed system well predicted all the sensory attributes except bitterness with
1031
acceptable r2. The precision and accuracy of built models were lower for chemical
1032
compounds responsible for taste attributes, except for tryptophan, succinate, lactate and
1033
ribose (Kang, Lee, & Park, 2014). The voltammetric Bio-e-tongue system was successfully
1034
investigated for prediction of phenolic compounds in rosé cava wines. The acquired results
1035
revealed excellent prediction with r, and RMSE in the range of 0.755 to 0.896, and 0.74 arb.
1036
unit to 41.4 mg L-1, respectively (Cetó, Capdevila, Mínguez, & del Valle, 2014).
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The red wines samples were discriminated using PCA analysis coupled with e-tongue
1038
technique. The analysis could classify wines based on their phenolic contents with PC1, PC2
1039
and PC3 explaining 85.8% of the total variance between red wine samples (Garcia-
1040
Hernandez, et al., 2020). The fermentation time (0, 2, 4, 6, 8, 10, 18, & 26 days) of
1041
pomegranate wine was also successfully discriminated using PCA (Lan, et al., 2017). The
1042
beer sample of different style was discriminated using LDA methods. The pruning step and
1043
windowed slicing integral method were used as a data reduction strategy. The correct
1044
classification rate of different beer styles was recorded at 81.9% for individual sample (Cetó
1045
& del Valle, 2014). The discrimination model for different commercial beers was developed
1046
using e-tongue system based on potentiometric ion-selective electrodes. The samples were
1047
analyzed without any pretreatment. The stepwise inclusion method based on Mahala Nobis
1048
distance criteria for selection of variables was used to capture the informative variables for
1049
discrimination. The LDA built model based on selected variables proved to be good
1050
alternative for the correct classification under complex situation (Cetó, et al., 2013). The
1051
LDA classification model established to identify different types of beers was able to
1052
discriminate samples with 100% accuracy (Blanco, et al., 2015). The classification of cava
1053
wines with variable ageing time was explored by employing voltammetric e-tongue. The
1054
acquired results achieved higher discrimination of cava wines according to the vintage time
1055
(Cetó, Capdevila, Puig‐Pujol, et al., 2014). The Chinese rice wines of different marked ages
1056
(1, 3 and 5) were also discriminated by PCA and CA, using voltammetric e-tongue system.
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The PCA scatter plot revealed clear discrimination for configuration of each sample with a
1058
total variance of 96.92% (Zhenbo Wei, et al., 2011). The taste sensing system was explored
1059
to discriminate three type of Chinese rice wines. Better results were achieved with DFA in
1060
classification of rice wines with different marked ages, whereas, PCA and DFA were similar
1061
in discrimination of wines with different flavors (ZhenBo Wei, et al., 2016). The
1062
voltammetric e-tongue system was further explored for identification of rice wines of
1063
different geographical origin. The nanocomposites modified electrode exhibited higher
1064
sensitivity to gallic acid, tyrosine, and guanosine-5-monophosphate disodium salt, having
1065
good correlation with geographical origin of wines. The area method was used to extract the
1066
informative variables from original data. The locality preserving projection presented better
1067
results for classification of geographical origin compared to that of LDA model (J. Wang, et
1068
al., 2019). The e-tongue system was also used to discriminate the red wine based on degree of
1069
maturation in barrels. The satisfactory results were obtained with LDA model accounting
1070
100% classification for different types of barrels used in wine maturation and ageing (Cetó, et
1071
al., 2017). A hybrid e-tongue system was explored for qualitative assessment of wine making
1072
process. The PCA and PLS-DA built models showed higher percentage of classification,
1073
confirming the reliability of hybrid e-tongue system for assessment of wine making (Kutyła‐
1074
Olesiuk, et al., 2018). In another study, voltammetric e-tongue system demonstrated better
1075
discrimination capability between apple liqueurs prepared from different apple varieties,
1076
when compared to that of Raman and UV-Vis spectroscopy (Śliwińska, et al., 2016). The
1077
Chinese rice wines sourced from different vintage year (2003, 2005, 2008, & 2010) were
1078
successfully discriminated based on taste-active compounds and the sensory attributes (Yu,
1079
Zhao, Li, Tian, & Ma, 2015). The portable multi-electrode e-tongue system was explored for
1080
discrimination of rice wines according to four different marked ages (3, 5, 8 and 10 years).
1081
Compared to other models, BPANN yielded better results with 100% success in both
1082
calibration and prediction sets (Ouyang, Zhao, & Chen, 2013). In another scientific opinion,
1083
e-tongue system based on voltammetric and potentiometric sensor array was used for
1084
identification of different style of beer. The information from both the sensors was
1085
preprocessed and combined to improve the classification ability of model. The results based
1086
on information obtained from both sensors were able to differentiate 100% of different style
1087
(lager, stout, & IPA) of beers as well as its production process (J. Gutiérrez, Moreno-Barón,
1088
Cetó, Mimendia, & del Valle, 2012). The different brands of Chinese rice wine were
1089
discriminated using ANN model with an average accuracy of more than 90% (Lu & Cai,
1090
2014). A method was developed for brand uniformity control of monoculture Apulian red
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wines (Primitivo and Negroamaro). The PLS-DA applied to output data permitted a correct
1092
discrimination of more than 70% in respect to original brand affiliation (Lvova, et al., 2018).
1093
The two different types of wines (white, and red) were discriminated using PCA and ANN.
1094
The proposed method was able to identify both categories yielding 98% success rate with
1095
ANN model (Díaz & Acevedo, 2014). In another study, e-tongue system coupled with LDA
1096
model was able to discriminate cava wines with 80-96% correct classification (Giménez-
1097
Gómez, et al., 2016). A clearer discrimination between dark and pale lager beer was observed
1098
using PCA (Arrieta, Rodríguez-Méndez, De Saja, Blanco, & Nimubona, 2010). Another low-
1099
cost method was proposed to discriminate whisky and wines using disposable voltammetric
1100
e-tongue system, fabricated with copper and gold substrates. The cheap and expensive
1101
whisky samples were successfully discriminated using only copper electrode. Likewise, e-
1102
tongue system based on both electrodes was able to discriminate different brands of wine and
1103
identify differences in the type of wine i.e. soft white, dry white, soft red, dry red
1104
(Novakowski, Bertotti, & Paixão, 2011). These studies may provide a valuable guideline for
1105
improving quality control, functional feature, discrimination, and authentication of alcoholic
1106
beverages through e-tongue.
lP
[Table 4 here]
3.7. Electronic nose
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Flavor is a complex phenomenon, different chemical compounds and sensory modalities
1110
influence the perception of flavor. The aroma or smell is largely attributed to the volatile
1111
compounds, considered to be major contributor to overall perception of flavor in alcoholic
1112
beverages (Polášková, et al., 2008). The progress on artificial olfactory instruments
1113
mimicking the biological system started with the discovery of materials having chemo-
1114
electronic properties (Röck, et al., 2008). Over past two decades, large number of scientific
1115
studies have been devoted towards the advancement of e-nose (Table 5). The aroma volatiles
1116
of Apulian wines made by autochthonous grape varieties (Primitivo and Negroamaro) were
1117
extracted by solid phase extraction (SPE) and investigated via GC-MS in combination with e-
1118
nose. The sensor data was analyzed, and good discrimination was achieved using PCA. The
1119
VOCs determined by GC-MS were predicted using PLS and RSR analysis. Satisfactory
1120
results were achieved using RSR technique for 18 wine odorant concentrations compared to
1121
PLS (Capone, et al., 2013). The spoilage induced by Brettanomyces yeast was also analyzed
1122
using MS e-nose instrument in two commercial red wine varieties in Australia (Shiraz, and
1123
Cabernet Sauvignon). The data analyzed through SLDA correctly classified 67% of the red
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33
wine samples in three different categories namely low (lower than 100 µg L-1), medium
1125
(between 500 to 200 µg L-1), and high (higher than 500 µg L-1), in respect to yeast spoilage.
1126
The concentration of 4-ethylphenol was also predicted using PLS built model with R2cal of
1127
0.70, and 0.90 for red wine sample in barrel and bottle, respectively (Cynkar, et al., 2007). A
1128
portable e-nose was used to classify rice wines with different marked ages (3, 5, 10, 15, and
1129
20 years). The LDA model based on feature extraction data provided the best discrimination
1130
results for different marked ages of rice wines. The marked ages were also successfully
1131
predicted with R2 0.9942, using SVM model (Zhebo Wei, Xiao, Wang, & Wang, 2017). A
1132
comparative study of metal oxide and mass spectrometry-based e-nose system was performed
1133
to predict two components of taints (4-ethylphenol, 4-ethylguaiacol), which are metabolites
1134
produced by Brettanomyces yeasts. The results reported that e-nose system based on metal
1135
oxide sensor was unable to classify “brett” spoilage owing to lack of gas sensor’s response to
1136
inter sample variation in volatile compounds other than ethylphenols. Contrarily, mass
1137
spectrometry-based e-nose system successfully predicted the concentrations of both
1138
components with high correlation coefficient (Berna, Trowell, Cynkar, & Cozzolino, 2008).
1139
In another study, better prediction results were obtained for sensory attributes and chemical
1140
components (Jesus Lozano, et al., 2007). A quantitative method was proposed for prediction
1141
of ageing time of sugar cane spirits in oak barrels using headspace-mass spectrometry e-nose
1142
system. The method was relatively simple and produced results for aging time with an
1143
accuracy of ≈ one month (Martí, Pino, Boqué, Busto, & Guasch, 2005). The Chinese
1144
premium liquors were successfully differentiated for their peculiar flavor style and origin
1145
using e-nose system combined with HCA and PCA. The 30 different volatile compounds
1146
were identified, which showed good synchronization with liquor characteristics (Xiao, et al.,
1147
2014). The potential of GC based e-nose system was explored for discrimination of rice wine
1148
age (1, 3, and 5 years). The results led to 96% correct classification using DA model (Yu,
1149
Dai, Yao, & Xiao, 2014). The flavor assessment of Chinese spirits was also carried out using
1150
e-nose system. The CA and PCA confirmed that S7 (sulphur-organic) and S9 (aromatics
1151
compounds, sulphur-organic) sensors could recognize Fougere-flavor and Feng-flavor, S1
1152
(aromatic compounds, toluene), S3 (aromatic compounds, ammonia), and S5 (alkenes,
1153
aromatic compounds), could identify Zhima, Mild, Strong, Jiang, and Nong jiang-flavor,
1154
while, S6 (broad-methane) could separate Laobaigan-flavor. The results reported that SVM
1155
built model was capable of differentiating Nong jiang, Zhima, Mild, Strong, and Jiang-flavor
1156
(M. Liu, et al., 2012). The metal oxide semiconductor-based e-nose has offered great
1157
capability for age fingerprinting of alcoholic beers with classification accuracy of 90%
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34
(Mahdi Ghasemi-Varnamkhasti, et al., 2011). The four types of Barbera wines procured from
1159
different geographical origin were classified using PCA, LDA, and CART analysis. During
1160
classification, best results were obtained from LDA, yielding 98.1% correct prediction and
1161
100% correct assignation (Buratti, Benedetti, Scampicchio, & Pangerod, 2004). Similarly,
1162
four types of Madrid wines were successfully identified using PCA and PNN model with
1163
excellent overall classification success (Santos, et al., 2004). The chaotic neural network
1164
entitled KIII was applied to e-nose technique for classification of six typical VOCs in
1165
Chinese rice wines. In comparison with BP-ANN, results indicated KIII network exhibited
1166
better performance in classification of VOCs with variable concentrations (Fu, Li, Qin, &
1167
Freeman, 2007). A nanostructure sensor based on gold doped zinc oxide was fabricated and
1168
used as a portable e-nose for discrimination of VOCs in red and white wines. The fabricated
1169
sensor was sensitive to VOCs, especially ethanol vapors. As per results, portable e-nose
1170
offered great capability to detect difference between alcoholic solutions and alcoholic
1171
beverages and differentiate red and white wines having same alcohol percentage
1172
(Wongchoosuk, Choopun, Tuantranont, & Kerdcharoen, 2009). The e-nose system in
1173
combination with HS-MS was even used to characterize and classify series of beers
1174
according to their chemical composition and production sites. From the results, it was visible
1175
that HS-MS e-nose offered great potential not only to differentiate beer samples but also
1176
delivered information about the specific compounds accountable for discrimination (Vera, et
1177
al., 2011). Likewise, the Tempranillo wines were successfully classified with good accuracy
1178
according to their geographical origin (Australia and Spain) using SLDA in combination with
1179
MS based e-nose system (Cynkar, Dambergs, Smith, & Cozzolino, 2010). The aroma
1180
characterization of alcoholic drinks is seemingly a difficult task owing to masking effect of
1181
ethanol. The e-nose system based on tin dioxide multisensor array was used to recognize 29
1182
different aroma compounds in white wine. The results indicated that in spite of great
1183
influence of ethanol and other wine compounds, the build system correctly classified added
1184
aromatic compounds to detect the adulteration with 97.2% of accuracy (J Lozano, Santos, &
1185
Horrillo, 2005). Likewise, off-flavor characterization of wines was successfully performed
1186
using dehydration and desalcoholization step through PCA method even at concentrations
1187
corresponding to perception threshold of human expert (Ragazzo-Sanchez, Chalier, &
1188
Ghommidh, 2005). In another scientific opinion, successful discrimination was carried out for
1189
different wines of the same grape variety using PCA coupled e-nose system based on surface
1190
acoustic wave devices (García, et al., 2006). The differentiation between fermented and non-
1191
fermented musts was also achieved in sweat wines using PCA method (García-Martínez, et
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35
al., 2011). The e-nose system offered greater perspectives to accurately differentiate between
1193
Cabernet Sauvignon wines produced from grapes that have received different pre-harvest and
1194
post bloom spray treatment to enhance their growth (Martin, Mallikarjunan, & Zoecklein,
1195
2008). The discrimination of two fungal species (Aspergillus niger, and Aspergillus
1196
carbonarius) responsible for contamination of wines were performed through volatile
1197
compounds they produce. The e-nose system based on metal oxide sensors coupled with PCA
1198
method was capable to discriminate between the two species within 48 h of growth on yeast
1199
extract sucrose and white grape juice agar, accounting more than 97% of the data. The
1200
differentiation was also confirmed by CA method (Cabañes, Sahgal, Bragulat, & Magan,
1201
2009). To control fraudulent practices, novel method was developed for detection and
1202
recognition of wine ageing. Purposely, two different measurement were performed; firstly,
1203
same wine was aged in different oak barrel (American and French oak), secondly wines
1204
procured from different wine cellar made with same grape variety aged in American and
1205
French oak. A classification success of 97% was recorded with ANNs method in detection of
1206
different ageing processes for the tested wines (J Lozano, et al., 2008). Previously, good
1207
discrimination for fermentation time of pomegranate wines was recorded with the use of PCA
1208
(Lan, et al., 2017). The PCA method also exhibited good discrimination for Spanish wines
1209
obtained form same viticulture zone and grape variety (Bellincontro, García-Martínez,
1210
Mencarelli, & Moreno, 2013). The simple classification through machine learning methods
1211
was evaluated for discrimination of beer using e-nose data. The acquired result for built
1212
models (LDA, SIMCA, PLS-DA, SVM) reported 100% classification accuracy for both
1213
training and test sets (Ghasemi-Varnamkhasti, Mohtasebi, Siadat, Ahmadi, & Razavi, 2015).
1214
However, a scientific study has also reported higher rate of misclassification for
1215
discrimination of geographical origin using metal oxide based e-nose (Berna, Trowell,
1216
Clifford, Cynkar, & Cozzolino, 2009). Conversely, clear visualization was observed among
1217
different classes of beer, wines, and spirit using PCA, and DFA methods (Ragazzo-Sanchez,
1218
Chalier, Chevalier, Calderon-Santoyo, & Ghommidh, 2008). An e-nose system using
1219
headspace as an extraction technique was used for identification and classification of
1220
aromatic compounds in red and white wines. The PCA showed clear separation, whereas,
1221
ANN revealed more than 98% success rate in classification of both wines (Jesús Lozano, et
1222
al., 2004). In another study, alcoholic beverages (wines, and beer) tainted with off-flavor
1223
were assessed through e-nose. The desalcoholization and dehydration procedure were
1224
performed before analysis. The off-flavor detection was successful in wines and beer samples
1225
using PCA and DFA method. Identification of differences among given beer and tainted beer
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of the two different brands was difficult than that of same brands (Ragazzo-Sanchez, et al.,
1227
2009). The portable e-nose system combined with hybrid carbon nanotube-SnO2 gas sensors
1228
was fabricated for detection of methanol contamination in whiskeys. The developed
1229
instrument employed feature extraction procedure including primary and integral derivatives,
1230
which lead to greater discrimination accuracy compared to that of classical feature (ΔR, and
1231
ΔR/R0). The proposed system offered great capability to monitor and classify pure and
1232
methanol contaminated samples (Wongchoosuk, Wisitsoraat, Tuantranont, & Kerdcharoen,
1233
2010). Similarly, two sensor arrays were developed for e-nose system, one with platinum and
1234
the other one with polysilicon integrated heater to monitor wine quality. The sensor array
1235
with polysilicon integrated heater reported 100% classification success using PNN for
1236
different kinds of wine (Aleixandre, et al., 2008). Similar results were also reported with
1237
correct classification rate of more than 90% of wines by their varietal origin using DPLS
1238
model (Cozzolino, Smyth, Cynkar, Dambergs, & Gishen, 2005). The effect of different
1239
temperatures on the headspace of wines was explored using MS based e-nose system. The
1240
fingerprints of volatiles captured from headspace were more affected in white wine, while red
1241
wine volatiles were seemingly less affected. This practical application demonstrated that care
1242
should be exercised in the selection of optimal temperature when recording fingerprints of
1243
wine using headspace (Cozzolino, Cynkar, Dambergs, & Smith, 2010). A comparation was
1244
made between human sensory panel and e-nose system for perception and recognition of
1245
thresholds of volatile components. The results revealed that the perception level of e-nose
1246
was lower in relation to human nose, albeit e-nose presented better output in recognition
1247
threshold of some aroma, offering great perspectives to be used as complementary tool to
1248
human sensory panels (Arroyo, et al., 2009). Conclusively, e-nose system based on different
1249
sensor array could be used for identification of origin, contamination detection, and
1250
authenticity assessment of alcoholic beverages at an industrial scale with full confidence.
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[Table 5 here]
1251 1252
3.8. Colorimetric sensor array
1253
The specialized brewing techniques and different raw material produce a variety of
1254
flavors, indicative of quality or grade of alcoholic beverage. The average price of alcoholic
1255
beverages might vary from few to thousands of dollars depending upon geographical origin,
1256
brand, and flavor. Thus, counterfeiting and adulteration of renowned brands to obtain higher
1257
profit has frustrated alcoholic beverage industry. In addition, increasing concerns have arisen
1258
about protection of geographical integrity as a part of agriculture policy in both European 37
1259
countries and P. R. of China. Therefore, reliable and efficient discrimination of alcoholic
1260
beverages carry an important cultural and economic value. Previously, colorimetric sensor array was fabricated for discrimination of commercial and
1262
high alcohol Chinese base liquors of different brands and flavors. The four chemical groups
1263
of volatile compounds were analyzed by GC-MS to serve as markers in base liquors. The
1264
developed calorimetric sensor array captured sensitive interactions with volatile markers. The
1265
results enabled identification of either commercial liquors of same flavor or base liquors with
1266
high-alcohol volume through color changes. The response of the sensor was greatly
1267
improved, and no misclassification was observed for both liquors (J.-J. Li, et al., 2014).
1268
Similarly, commercial beers were investigated in gas and liquid phases using colorimetric
1269
sensor array. The results yielded facile identification of beers in either liquid or gas phase
1270
through comparison of the color changes by means of HCA statistical analysis (Zhang,
1271
Bailey, & Suslick, 2006). In another study, colorimetric sensor array was used to determine
1272
astringency, sourness, and sweetness of white wines by mean of PCA and ANN model. The
1273
PCA reduced color variables to three PCs and differentiated between less sweet and sweet
1274
sets of wines. The ANN model quantitatively predicted the astringency, sourness, and
1275
sweetness with higher correlation coefficient. The colorimetric sensor array of higher
1276
potential used in industries for identification of taste components can provide the level of
1277
astringency, sourness, and sweetness to the consumers (Chung, et al., 2015). The colorimetric
1278
artificial nose was also used to identify and characterize Chinese liquors procured from
1279
different geographical regions. Data indicated that each category of liquor could be grouped
1280
together using PCA and HCA plot. The LDA model reported 100% correct classification for
1281
different categories of Chinese liquors (Qin, et al., 2012). Likewise other study also reported
1282
classification accuracy of 100% for identification of different baijiu styles by mean of LDA
1283
model (Ya, et al., 2012). The authors reported discrimination of commercial baijiu using
1284
fabricated colorimetric sensor array. From the analysis, eight types of baijiu samples were
1285
distinguished significantly by mean of PCA, LDA and HCA without misclassification
1286
(Zheng, et al., 2018). This summary about colorimetric sensor array could be helpful for
1287
quality control of alcoholic beverages in the market and mass production factories.
1288
3.9. Combination of Sensors
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1289
The utilization and advancement in different sensors have made available more and more
1290
data for integration of two or more sensor fusion for innovative results. The fusion of data
1291
from different sensors are said to be analogous to the cognitive process, that is utilized by
1292
human to continually combine the data from different senses to make inferences about 38
surrounding environment (Xiaobo & Jiewen, 2005). The processing of integrated data
1294
acquired from different techniques or sensors is known as data fusion (Hall & Llinas, 1997).
1295
The literature survey related to combination of sensors for quantitation, discrimination, and
1296
authentication of alcoholic beverages has been presented in Table 6. Previously, application
1297
of ATR-IR, Raman spectroscopy and concatenated ATR-IR and Raman spectroscopy were
1298
investigated for determination of total phenolic content and antioxidant capacity of Chinese
1299
rice wine. The combinations of SiPLS, SVM and PCA were applied to process the combined
1300
data from these nondestructive instrumental techniques. The SVM models based on efficient
1301
spectral intervals selected by SiPLS from ATR-IR and Raman spectra were superior to PLS
1302
models based on the same captured variables, and PLS models based on ATR-IR or Raman
1303
spectra (Wu, Xu, et al., 2016). The combination of NIR and MIR spectroscopy was employed
1304
for quantitation of real extract, original extract, and alcohol content in beer. Slightly better
1305
prediction performance was recorded with combination of NIR and MIR spectral data using
1306
ANN model for all the parameters. The RMSEP for determination of ethanol, real extract and
1307
original extract ranged from 0.076% v/v to 0.14% v/v (Iñón, Garrigues, & de la Guardia,
1308
2006). The integration of e-nose and e-tongue sensors was used to predict sensorial
1309
descriptors of Italian red dry wines. The results reported better prediction of large part of the
1310
selected sensorial parameters except sourness by mean of genetic algorithms (Buratti, et al.,
1311
2007). Likewise, combination of e-nose and e-tongue sensors based on arrays of
1312
metalloporphyrin was explored to predict sensory descriptors and 23 chemical parameters.
1313
The combination of two arrays enhanced the prediction performance of build models for both
1314
quantitative and qualitative parameters (Di Natale, et al., 2004). In another scientific opinion,
1315
an electronic panel (e-nose, and e-tongue) was explored to analyze oxygen related
1316
parameters, polyphenols and color indices in red wines. The obtained results yielded higher
1317
correlation coefficient for calibration and prediction sets with RMSEP ranging from 0.193
1318
mg L-1 to 166.37 mg L-1 for all the parameters using PLS1 model (Rodriguez-Mendez, et al.,
1319
2014).
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1320
Likewise, an electronic panel based on e-tongue and e-nose sensors was explored for
1321
prediction of different classes of wines. The prediction and validation models developed
1322
using electronic panel indicated better capability of system to discriminate wines according to
1323
the use of micro-oxygenation by mean of PLS-DA model (Rodriguez-Mendez, et al., 2014).
1324
The combination of e-nose and e-tongue based on two arrays of metalloporphyrin was also
1325
successfully used to discriminate different wine classes by mean of PLS-DA model (Di
1326
Natale, et al., 2004). The combination of data acquired using e-tongue, e-nose and e-eyes was 39
used to investigate the aging of red wine. The electronic panel test in combination with PLS-
1328
DA model outperformed in discriminating wines when compared to traditional chemical
1329
analysis. Successful discrimination after ten months of aging was also observed between
1330
wines soaked with oak chips and the ones aged in French oak barrel of the same toasting
1331
level and origin, and treated with micropigmentation (Apetrei, et al., 2012). The fusion of
1332
three sensory tools; an array of electrochemical liquid sensors, an array of gas sensors, and an
1333
optical system was evaluated for discrimination of wines. The discrimination ability of
1334
system was greatly improved when data from each sensor was fused to form a multimodal
1335
feature vector (Rodríguez-Méndez, et al., 2004). The accumulated data from e-nose and e-
1336
tongue was used for classification of beer flavor. The PCA, GA-PLS, and variable
1337
importance of projection scores were applied to capture important information from the
1338
original fusion set to build robust models. The results showed that best classification was
1339
observed with ELM model built on selected feature variables (Men, et al., 2017). The hybrid
1340
e-tongue system based on data fusion of two sensor families was explored for discrimination
1341
of different beer types. The sensor arrays comprised of 15 potentiometric sensors and 6
1342
modified graphite-epoxy voltammetric sensors. The acquired results yielded successful
1343
qualitative classification by exploiting the new approach of data integration from two
1344
different sensors (J. M. Gutiérrez, et al., 2013). In another study, combination of UV, Vis,
1345
NIR and MIR spectroscopy was investigated to distinguish commercial Sauvignon Blanc
1346
wines from New Zealand, and Australia. The reported results showed better discrimination of
1347
geographical origin by the PLS-DA model based on concatenated MIR and NIR
1348
spectroscopy. The samples procured form Adelaide Hills of South Australia were
1349
misclassified (Cozzolino, et al., 2011a). Similarly, four types of barbera wines produced in
1350
northern Italy in an enclosed geographical areas were successfully classified by mean of LDA
1351
built model based on e-nose and amperometric e-tongue data (Buratti, et al., 2004). A clear
1352
discrimination of wine deterioration as a function of time was observed using sensor fusion of
1353
e-nose and e-tongue (Gil-Sánchez, et al., 2011). In conclusion, multisensor fusion approach
1354
offers good perspectives to be used as rapid tool to identify geographical region, quality
1355
control analysis, and safety assessment of alcoholic beverages.
1356 1357
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[Table 6 here] 4. Technical challenges and future perspectives
1358
During last two decades, nondestructive instrumental techniques or sensors have offered
1359
endless possibilities to investigate and predict changes related to quality control, adulteration 40
1360
assessment, geographical discrimination, and flavor profile of alcoholic beverages. The
1361
qualitative knowledge about key components of alcoholic beverages is very important but not
1362
enough to predict organoleptic and technological properties of individual brands or labels.
1363
The development of chemometrics coupled with nondestructive instrumental techniques or
1364
fabricated sensors allows the use of these methods as a rapid tool for in-line monitoring of
1365
quality parameters of alcoholic beverages. The cost saving, time reduction, environment
1366
friendliness, and potential saving nature of nondestructive instrumental techniques had
1367
brought them in limelight for analysis of alcoholic beverages. There is no doubt about the tremendous potential of nondestructive instrumental
1369
techniques, however, specific mathematical knowledge is required for development of robust
1370
models for the analysis of alcoholic beverages. Moreover, owing to different scale of signal,
1371
collinearity among variables, possible baseline shift, and instrumental drift, the chemometric
1372
analysis require extensive data pretreatment. In chemometrics, the manipulating approach is
1373
also used to enhance the prediction precision and model accuracy for simultaneous
1374
applications. In addition, high cost of the technologies and scientific developments attained at
1375
laboratory level are the major hurdles in their adaptation at industrial level. Accordingly,
1376
alcoholic beverage industry can practically seize the opportunity to implement these
1377
nondestructive instrumental techniques without requiring extensive and inefficient chemical
1378
analysis, thus improving the quality of drinks.
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Currently, the interest in producing alcohol free or low-alcoholic drinks from the
1380
alcoholic beverages is the biggest challenge for food processers owing to the growing health
1381
concerns associated
1382
dealcoholization methodologies such as distillation or evaporation efficiently remove ethanol,
1383
however, some organoleptic compounds may also be lost during the process, affecting the
1384
quality of the beverage (Castro-Muñoz, Galiano, & Figoli, 2019). Purposely, membrane-
1385
based technologies should be promoted in the alcoholic beverage industry to remove ethanol
1386
while retaining the quality of the alcoholic beverage (Castro-Muñoz, 2019a). Pervaporation is
1387
one such technique used not only for dealcoholization of alcoholic beverages but also for
1388
aroma recovery, thus meeting the quality parameters of non-alcoholic or low-alcoholic
1389
beverages (Castro-Muñoz, 2019b).
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1379
with the consumption of alcohol. Albeit, post-fermentation
1390
Moreover, other membrane-based technologies such as ultrafiltration, microfiltration,
1391
membrane distillation, and nanofiltration could be explored to improve the quality of
1392
alcoholic beverages. Various applications of these innovative techniques have been
1393
developed such as extraction or purification of high value biomolecules from agro-industrial 41
1394
byproducts, recovery of aromatic compounds from processed and natural products, treatment
1395
of natural extracts, and production of non-alcoholic beverages being the popular ones
1396
(Castro-Muñoz, Boczkaj, Gontarek, Cassano, & Fíla, 2020; Castro-Muñoz, Díaz-Montes,
1397
Cassano, & Gontarek, 2020; Díaz-Montes, Gutiérrez-Macías, Orozco-Álvarez, & Castro-
1398
Muñoz, 2020). Additionally, one of the few limitations of nondestructive instrumental techniques is their
1400
inability to quantify lower concentration of analyte in the sample. For instance, very low
1401
concentration of ethanol in dealcoholized beverages may not be predicted using
1402
nondestructive instrumental techniques. Therefore, future work should focus on improving
1403
nondestructive instrumental techniques while enhancing the efficiency of developed
1404
algorithms leading to improved prediction accuracy of the build model for low concentration
1405
analytes and possible implementation of these techniques at industrial level.
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1399
The current review will open new avenues for the application of advanced green
1407
nondestructive instrumental techniques for monitoring of alcoholic beverages. The future
1408
research should be explored to improve and extend the area of its applications. The academic
1409
and field training should be provided for the implementation of nondestructive instrumental
1410
techniques as analytical tool for monitoring quality parameters of alcoholic beverages. In
1411
order to achieve reproducible discrimination for future applications, many types of flavor and
1412
classes of alcoholic beverage from different countries should be harmonized. The cheaper
1413
and portable nondestructive instrumental techniques are expected to further improve their
1414
accessibility to smaller industries. A single step algorithm is the future of rapid screening of
1415
various compounds having multiple health benefits. The studies should also be focused on in-
1416
line monitoring of alcoholic beverages with wide combinations of chemometric techniques.
1417
The future work is expected to establish valuable algorithms present in other fields for their
1418
implantation in the analytical field of alcoholic beverages.
1419
5. Conclusion
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1420
This review article has primarily outlined guidelines and recent advances in quality
1421
control, discrimination and adulteration detection of alcoholic beverages. The nondestructive
1422
instrumental techniques simultaneously realizing detection of various quality parameters,
1423
monitoring
1424
differentiating different flavor or classes of alcoholic beverages are thoroughly highlighted.
1425
The major advances in the implementation of these techniques for the analysis of alcoholic
1426
beverages have synchronized with the developments in powerful chemometrics. These
1427
methods allow capturing of related information from complex and large data for quantitative
fermentation
attributes,
discrimination
of
geographical
regions,
and
42
1428
prediction and qualitative analysis of alcoholic beverages. However, investigations targeting
1429
availability of nondestructive instrument, development of representative databanks, diversity
1430
of applications, and better understanding of the technology should form the basis of future
1431
research explorations.
1432
Compliance with Ethical Standards:
1433
Acknowledgments The authors appreciatively acknowledge financial support provided by the National
1435
Natural Science Foundation of China [31750110458, 31671844, and 31601543]; National
1436
Key Technology Research and Development Program of China [2018YFD0400803,
1437
2017YFC1600805, 2017YFC1600806, 2016YFD0401104]; and the China Postdoctoral
1438
Science Foundation [2017M611736]. We also would like to thank our colleagues in School
1439
of Food and Biological Engineering who helped in this work.
1440
Conflict of interest
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-p
All the authors declared no conflict of interest.
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Table 1. Application of Vis-NIR, UV-VIS-NIR, and NIR spectroscopy for quantitation, discrimination, and authentication of alcoholic beverages Beverage type
Parameters
Vinho verde wines Dukang base liquor
Quantification of 10 volatile compounds (mg L-1)
Red wines
3-methyl-1-butanol (mg/100mL) 1-butanol (mg/100mL) 1-propanol (mg/100mL) Polyphenolic compounds (mg L-1)
No of sample/ Division/Quantificati on 89-99/, 2/3 Calibration, 1/3 Validation/ GC-MS 81/61:20/ GC-MS
Mode/Wavelen gth range
Pretreatment
Chemome trics
Optimal model
Precision
Reference
Transmission/ 600-14000 cm-1
SNV
PLS
PLS
R2= 0.94-0.97, RMSE=3.9-42.5
(Genisheva, et al., 2018)
Transmittance/ 4000-12000 cm-1
1st Dev, 2nd Dev, SLS, MSC
PLS
PLS
R2=98.05, RMCEC=0.67, R2=95.22, RMSEP=1.35 R2=98.05, RMCEC=0.49, R2=97.96, RMSEP=0.81 R2=95.21, RMCEC=0.27, R2=94.72, RMSEP=0.40
(Han, Zhang, Li, Li, & Liu, 2016)
78/ HPLC
Transmittance/ 190-2500 nm
1st Dev, 2nd Dev, SNV, MSC, Savitzky-Golay smoothing, Smoothing & Normalize -----
PCR, PLS
PLS
r2cal range=0.41-1 RMSEC range=0.11-158.58 RMSECV range=0.14-0.88
(Martelo-Vidal & Vázquez, 2014)
PLSR
PLSR
(Shen, et al., 2010)
PLSR, LSV-SVM
LSVSVM
2nd Dev, SNV
PLS
PLS
rcal > 0.94) for all amino acids except proline, histidine, and arginine rcv >0.81 for 12 amino acids rval=0.915, RMSE=0.168 rval= 0.888, RMSE= 0.146 rval= 0.872, RMSE= 0.033 R2val= 0.90, SECV=9.80 (Calcium) R2val= 0.86, SECV= 0.65 (Iron) R2val= 0.89, SECV= 147.6 (Potassium) R2val= <0.80 (other minerals)
Amino acids (mg/L)
98/ HPLC
Absorbance/ 800-2500 nm
Chinese rice wine
Alcohol content (%) Titratable acidity (g L-1) pH Calcium, Potassium, Magnesium, Phosphorus, Sodium, Sulphur, Iron, Boron and Manganese (mg L-1) Total reducing sugar (g L-1) Amino acid nitrogen (g L-1) pH
138/92:46 Distillation, titration, Potentiometry 126/63:63/ Inductively coupled plasma atomic emission spectrometry 156/117:36/ Colorimetry, titration, pH meter
Transreflectance / 600-1200 nm Transmittance/ 400-2500 nm
Transmission/ 10000-4000 cm-1
1st Dev, 2nd Dev, SNV, MSC
PLS, SiPLS, SVM, SiSVM
SiSVM
Apple wine
Soluble solid content (%) Total acidity (g/100 mL) Total ester content (g/L) pH
Transmission/ 4000–12000 cm−1
1st Dev, 2nd Dev, MSC,
PLS
PLS
Chinese rice wines
Transmission/ 800-2500 nm
-----
Rice wines
Potassium (mg L-1) Magnesium (mg L-1) Zincum (mg L-1) Iron (mg L-1) SSC (˚Brix) pH
380/288:92/ Hand-held refractometer, Titration, pH meter 38/29:9/ Atomic absorption spectroscopy
Transmission/ 325-1075 nm
Savitzky-Golay, SNV
PLS
PLS
Apple wine
Alcohol strength (mL/L) Titratable acidity (g/L) Glucose (g L-1), fructose (g L-1), ethanol (g L-1), Glycerol (g L-1), total phenolics (mg L-1), Total anthocyanins (mg L-1), Total flavonoids (mg L-1), Alcohol content (%), Titratable acidity (g L-1) pH Alcohol content (%), Titratable acidity (g L-1) pH Malvidin-3-glucoside (mg L1 ), pigmented polymers (mg L-1), tannins (mg L-1) Ethanol (%)
Transmission/ 4000–12000 cm−1 Transmission/ 12500-3600 cm-1
2nd Dev, Straight line subtraction SNV, MSC, Derivative transformation
PLS
Red wine
300/240:60/ Refractometer, pH meter 160/120:40/ PT-1 Alcoholmeter, Titration 75/ HPLC, Spectrophotometer
(Cozzolino, et al., 2008)
(Wu, Long, et al., 2015)
oo
-p r
Rcal=0.958, RMSEC=12.10, Rval=0.861, RMSEP=16.90 Rcal=0.885, RMSEC=3.78, Rval=0.700, RMSEP=4.17 Rcal=0.351, RMSEC=0.25, Rval=0.621, RMSEP=0.13 Rcal=0.403, RMSEC=0.73, Rval=0.584, RMSEP=0.58 r= 0.95, SEP=0.16, RMSEP=0.17 r=0.94, SEP=0.02, RMSEP=0.02
(F. Liu, He, Wang, & Pan, 2007)
PLS
Rc2=0.923, RMSECV=4.63, Rp2=0.993, RMSEP=4.25 Rc2=0.931, RMSECV=0.264, Rp2=0.973, RMSEP=0.21
(Peng, Ge, Cui, & Zhao, 2016)
PLS
PLS
Calibration and cross validation range rcal=0.97-0.99, RMSEC=0.41-217, rcv=0.93-0.99, RMSECV=0.44-229,
(Di Egidio, Sinelli, Giovanelli, Moles, & Casiraghi, 2010)
-----
PLSR, LS-SVM
LSSVM
(Yu, Niu, Ying, & Pan, 2008)
-----
PLS, LSSVM
LSSVM
re
PLSR
(Ye, Yue, Yuan, & Li, 2014)
(Yu, Zhou, Fu, Xie, & Ying, 2007)
2nd Dev, scatter correction
PLS
PLS
Transmission/ 350-1200 nm
1st Dev, 2nd Dev
PLS
PLS
Rcal=0.892, RMSEC=0.16, RMSECV=0.29
(Ying, Yu, Pan, & Lin, 2006)
Alcohol degree (%), pH, Total acid (g L-1), amino acid nitrogen (g L-1), ˚brix (%) Total acid (mol/L), Total sugar (g/L), Alcohol content (v/v)
100/ National Standard of China GB/T 136622000 88/ GB/T13662-2000 & GB 17946-2000 official methods 91/ GB/T15038-2006 official method
Transmission/ 800-2500 nm
2nd Dev
PLS
PLS
R2cal =0.83-0.96, r2val=0.77-0.92 Prediction of amino acid nitrogen was the worst & further study needed
(Yu, Ying, Fu, & Lu, 2006)
Transmittance/ 400-2400 nm
1st Dev, 2nd Dev, SNV, MSC, OSC
ACOPLS
R=0.987, RMSE=0.001, R=0.973, RMSE=0.001 R=0.999, RMSE=0.082, R=0.994, RMSE=0.089 R=0.943, RMSE=0.187, R=0.928, RMSE=0.206
(Hu, Yin, Ma, & Liu, 2018)
Apple wine
Detection of volatile compounds (mg/L)
72/42:30 HS-SPME–GC–MS
Transmittance/ 4000-12000 cm-1
PLS
R2c=0.8844-0.9497, RMSEC=0.023-37.50, R2cv=0.8278-0.8916, RMSECV=0.025-49.60
(Ye, Gao, Li, Yuan, & Yue, 2016)
Apple wine
Decanoic acid (mg/L), Hexanoic acid (mg/L), Octanoic acid (mg/L)
72/42:30 HS-SPME–GC–MS
Transmittance/ 4000-12000 cm-1
PLS
PLS
Ethanol content (g/L) Total acid (g/L)
144/108:36 HPLC, titration
Transmittance/ 4000-10000 cm-1
GASVM
Rice wine ‘Makgeoll i’
Alcohol (%), Reducing sugar (%), Titratable acid (%)
132/105-106:26-27 HPLS, Copperbicinchoninate method, Titration
Transmission/ 4000-10000 cm-1
RCA-PLS, SPA-PLS, iPLS, GA, RCASVM SPA-SVM iPLS-SVM GA-SVM PLSR
R2c=0.9007, RMSEC=0.40, R2cv=0.8590, RMSECV= 0.44 R2c=0.9179, RMSEC=0.74, R2cv=0.8680, RMSECV=0.85 R2c=0.9118, RMSEC=0.89, R2cv=0.8539, RMSECV=1.05 R2=0.97, RMSEC=2.42, R2=0.94, RMSEP=3.02 R2=0.97, RMSEC=0.09, R2=0.97, RMSEP=0.10
(Ye, et al., 2016)
Chinese rice wine
1st Dev, 2nd Dev, MSC, VN, MMN, ECO, MSL 1st Dev, 2nd Dev, MSC, VN, MMN, ECO, MSL 1st Dev, SNV, MSC, DT, BL, Savitzky-Golay
PLS, CARSPLS, ACO-PLS PLS
PLSR
R2p=0.973, SEP=0.760% (normalization) R2p=0.945, SEP=1.233% (no data preprocessing) R2p=0.882, SEP=0.045% (normalization)
(Kim & Cho, 2015)
Rice wine
Alcohol content (%) Titratable acidity (g L-1) pH
100/75:25 Distillation/aerometry, titration, potentiometry
500-1200 nm
PLSR, LS-SVM
LSSVM
Rcal=0.963, RMSEC=0.09, rval=0.960, RMSEP=0.11 Rcal=0.952, RMSEC=0.06, rval=0.942, RMSEP=0.07 Rcal=0.959, RMSEC=0.01, rval=0.866, RMSEP=0.02
(Yu, Niu, Ying, & Pan, 2011)
Chinese rice wine
Chinese rice wine
Wine
Transmission/ 800-2500 nm
Jo
Red wine
ur Transmission/ 400-2500 nm
Rice Wine
360-2000 nm
R2= 0.9408, RMCECV= 2.15, R2= 0.9214, RMSEP= 2.07 R2= 0.9356, RMCECV= 0.03, R2= 0.9120, RMSEP= 0.03 R2= 0.9407, RMCECV=0.10, R2= 0.9302, RMSEP=0.10 Rc2= 0.72, RMSECV=1.45, Rp2= 0.76, RMSEP=1.48 Rc2= 0.91, RMSECV=0.05, Rp2= 0.94, RMSEP=0.04 Rc2= 0.72, RMSECV=0.21, Rp2= 0.91, RMSEP= 0.10 Rc2= 0.84, RMSECV=0.11, Rp2= 0.87, RMSEP=0.10
(Yu, et al., 2009)
rcal=0.963, RMSEC=0.09, rval=0.960, RMSEP=0.11 rcal=0.952, RMSEC=0.06, rval=0.942, RMSEP=0.07 rcal=0.959, RMSEC=0.01, rval=0.866, RMSEP=0.02 R2val=0.91, RMSEP=0.09 R2val=0.82, RMSEP=0.05 R2val=0.96, RMSEP=0.01 R2cal>0.80, SEC=3.1-55.7, SECV=3.2-61.7
Rice wine
100/75:25 Distillation, titration, Potentiometry 147/98:49 Distillation, titration, Potentiometry 495/ HPLC
PLSR
lP
Chinese rice wine
na
Australian wines
-----
f
Chinese rice wine
1st Dev, 2nd Dev, Normalization, MSC, SNV, Norris-Gap, Savitzky-Golay -----
(Yu, Lin, et al., 2008)
(Cozzolino, et al., 2004)
(Wu, Xu, Wang, et al., 2015)
Beer
Soluble solids content (%)
360/180:90:90 Refractometer
Transmission/ 325–1075 nm
1st Dev, 2nd Dev, SG, SNV
Shaoxing & Chinese rice wines Chinese liquors
Fraudulent detection
54/36:18
Transmission/ 800-2500 nm
2nd Dev, SNV, MSC
Authenticity & adulteration 42 samples without marked age 78 samples with marked age Fermentation stage classification (0-3, 4-6, 7-9, & 10-20 days Brands, alcohol degree, age & six flavors
120/78:42
Transmission/ 4000-12000 cm-1
-----
156/117:36
Transmission/ 10000-4000 cm-1
730/488:244
Absorbance/ 570-1848 nm
Wine
Australian and New Zealand origin classification
64/34:30
Whiskey, brandy, rum & vodka White and red wines
Adulterated with 5% and 10% (v/v) of water, ethanol or methanol
69/40:24:5
Transmission/ Vis & NIR 400– 2500 nm; UV-Vis MIR 400-4000 cm-1 1100 to 2500 nm
Denomination of origin, 82 white wine 153 red wine
235/
Absorbance/ 200 to 800 nm
Chinese rice wines Shiraz wines
Discrimination of geographical origin Four Western Australia and Coonawarra were separated from other wines
38/29:9/
Transmission/ 800-2500 nm Transmission/ 400-2500 nm
Chinese rice wines
Discrimination of geographical origins
112/84:28
Absorbance/ 190–800 nm
Whisky
Discrimination of whisky brands and counterfeit identification Discriminate between commercial white wines of different varietal origins
237/158:79
Absorbance/ 190-1100 nm
1st Dev, Savitzky-Golay
269/136:133
Transmission/ 400-2500 nm
Classify the geographical origin of wines from Australia and Spain Fermentation monitoring (0, 2, 5,7 and 30–35 days)
63/32:31
2nd Dev, smoothing, Savitzky-Golay derivation 2nd Dev, SNV,
Beer
Qualitative characterization of five aging categories of beer
139
Rice wine
Discrimination of rice wine age (1, 3, & 5) Classify wines from Australia, New Zealand, France & Germany Discrimination inside a controlled designation of origin
Riesling wines Wine
DA, DPLS
Calibration= 97.2%, Validation= 100%
(Shen, et al., 2012)
PCA, SVM
SVM
Accuracy, sensitivity and specificity were 94.9%, 93.1% & 97.9%, respectively
(H. Chen, Tan, Wu, Wang, & Zhu, 2014)
1st Dev, 2nd Dev, SNV, MSC
PCA, PLSDA
PLS-DA
Calibration=92.3%, Prediction=89.7%
(Wu, Long, et al., 2015)
cubic-order Savitzky-Golay smoothing 4, 8, 16, and 32 points, 1st Dev, 2nd Dev, -----
SVM, SIMCA, PCA-LDA
PCALDA
Discrimination range 95.65–100 % in training and test set
(Li, et al., 2014)
PCA, SIMCA, PLS-DA
PLS-DA
Overall classification UV–VIS=67 NIR=76 MIR=90
(D Cozzolino, WU Cynkar, N Shah, & PA Smith, 2011a)
PCA SIMCA
SIMCA
100% of correct prediction, at a confidence level of 95%
(Pontes, et al., 2006)
SVM, SIMCA, PLS-DA, NN-MLP, & k-NN PCA, PLSR PCA, LDA, SIMCA
SVM
Classification rates above 96% achieved
(Acevedo, Jiménez, Maldonado, Domínguez, & Narváez, 2007)
LDA, PLS-DA, SVM, SIMCA PLS-DA
LDA
Training set= 98.96%, Testing set=100%
(Wu, Li, et al., 2015)
PLS-DA
Classification rates for genuine and false samples over 98.6% and 93.1%, respectively
PCA, PCR, DPLS
DPLS
Correctly classified 100% of Riesling & up to 96% of Chardonnay wines
(Martins, Talhavini, Vieira, Zacca, & Braga, 2017) (Cozzolino, Smyth, & Gishen, 2003)
PCA, LDA, PLS-DA PCA, LDA
PLS-DA
Correctly classified 100% and 84.7% of the Australian and Spanish wine samples, respectively
LDA
Correctly classified range 78.6 to 100% in calibration and prediction set
SELEC T+LDA
Correct predictions range 62-86%
(GhasemiVarnamkhasti & Forina, 2014)
LSSVM SLDA
Correctly classified range 93.75-100%
(Yu, Lin, et al., 2008)
SLDA calibration models correctly classified 86%, 67%, 67% and 87.5% of the Australian, New Zealand, French and German Riesling wines, respectively Condado Salnés and Ribeira de Ulla was 100% classified by applying SIMCA Rosal wines was 100% classified by applying LDA
(L. Liu, et al., 2008)
lP
Transmission/ 400-2500 nm
75
Transmission/ 12500-3600 cm-1 12500-5405 cm-1
147/98:49 50
33
oo
(Yu, et al., 2007)
LDA
NIR range 1300-1650 nm provide best results with 100% classification Overall correct classification rate 60%
re
2nd Dev, SNV, Savitzky-Golay derivation & smoothing 1st Dev, 2nd Dev, SNV, MSC & 9 points smooth
PLSR
-p r
-----
SNV, MSC, Derivative transformation -----
Transmission/ 800-2500 nm transmission/ 400-2500 nm
-----
Transmission/ 190-2500 nm
2nd Dev, SNV, Smoothing, Savitzky-Golay
-----
f
(F. Liu, Jiang, & He, 2009)
na
Riesling & chardonna y wine Tempranil lo red wines Red wine
98
r=0.9905, RMSEC=0.11, r=0.9915, RMSEV=0.10, r=0.9818, RMSEP=0.16
Savitzky-Golay second-order polynomial fitting -----
ur
Chinese liquor
SPALSSVM
Jo
Chinese rice wine
SPA, RCA, ICA, PLS, MLR, LSSVM PCA, DA, DPLS
PCA, LDA, StepLDA, KNN,GA, SELECT PCA, DA, LS-SVM PCA, PLSDA, SLDA PCA, LDA, SVM, SIMCA
LDA, SIMCA
(Riovanto, Cynkar, Berzaghi, & Cozzolino, 2011)
(L. Liu, Cozzolino, Cynkar, Gishen, & Colby, 2006) (Di Egidio, et al., 2010)
(Martelo Vidal, Domínguez Agis, & Vázquez, 2013)
Table 2. Application of MIR spectroscopy for quantitation, discrimination, and authentication of alcoholic beverages Beverage type
Parameters
Red wines
TPI280 Folin-Ciocalteu index glories color parameters CIELab color parameters
Straw wine
Alcohol content (%), Sugar content (g/L), Titratable acidity (g/L)
Cabernet sauvignon wines
Total phenolic (mg GAE/L), Anthocyanins (mg malvidin-3glucoside/L), Tannins (g cyanidin/L), Flavonoids (mg GAE/L), ABTS & DPPH assays (mmol
No of sample/division /Quantification 36/ Spectrophotometer, Color meter
302/244:58 115/90:25 107/87:20/ Distillation, HPLC, 30/24:6 Spectrophotometer
Mode/Wavelength range
Pretreatment
Transmittance/ 4000-400 cm-1
-----
Chemometrics
PLS-1
Optimal model PLS-1
Transmission/ 1000-300 cm-1
OSC
PLSR
PLSR
4000-550 cm-1
1st Dev, 2nd Dev, SNV, MSC, Savitzky-Golay
PLS1, PLS2
PLS1
Precision
Reference
Rc2=0.9195, RMSEc=2.22, Rp2=0.8908, RMSEp=2.70 Rc2=0.9029, RMSEc=2.19, Rp2=0.8538, RMSEp=2.81 Rc2>0.93, RMSEc<1.11, Rp2>0.91, RMSEp<1.32 Rc2>0.92, RMSEc<2.17, Rp2>0.90, RMSEp<2.49 R2pred=0.99, RMSEP=0.28 R2pred=0.98, RMSEP=9.9 R2pred=0.92, RMSEP=0.29
(Garcia-Hernandez, SalvoComino, Martin-Pedrosa, GarciaCabezon, & Rodriguez-Mendez, 2020)
Rv2=0.9301–0.9502 Rc2=0.9389-0.9480
(Grijalva-Verdugo, HernándezMartínez, Meza-Márquez, Gallardo-Velázquez, & OsorioRevilla, 2018)
(Croce, et al., 2020)
PLS, iPLS, SVM, ISVM
ISVM
Ethanol (%)
57/ GC FID
1200-850 cm-1
1st Dev, 2nd Dev, Normalization, Mean centering
PLS
PLS
Methanol (g/hL), Acetaldehyde (g/hL), Fusel alcohols (g/hL), Ethyl acetate (g/hL), Alcoholic strength (%) Total reducing sugar (g L-1) Amino acid nitrogen (g L-1) pH
52-166/41-83:42-83/ GC-FID, Distillation
4000-400 cm-1
1st Dev, 2nd Dev, MSC, SLS
PLS
PLS
156/117:36/ Colorimetry, Titration, pH meter
Transmission/ 4000-800 cm-1
1st Dev, 2nd Dev, SNV, MSC
PLS, SiPLS, SVM, SiSVM
SiSVM
Glucose (g L-1), fructose (g L-1), ethanol (g L-1), Glycerol (g L-1), total phenolics (mg L-1), Total anthocyanins (mg L-1), Total flavonoids (mg L-1) Alcohol (%), Reducing sugar (%), Titratable acid (%)
75/ HPLC, Spectrophotometer
700-4000 cm-1
SNV, MSC, Derivative transformation
PLS
PLS
129/96:33 HPLC, Copperbicinchoninate method, titration 100 chloride/ 125 sulfates/ UV/Vis Spectrophotometer 79/52:27 UV-visible Spectrophotometer, pH differential method, Refractometer, Titration, pH meter, 79/52:27 UV-visible Spectrophotometer, Conductivity meter, HPLC, pH meter, Refractometer 80/60:20 Chinese National Standard GB-13662–2008 130/100:30
Transmittance/ 400-4000 cm-1
1st Dev, 2nd Dev, Norris-Gap, Savitzky-Golay
PLSR
PLSR
R2=0.984, SEP= 0.595 R2=0.983, SEP=0.579 R2=0.936, SEP=0.026
f
(Kim, et al., 2016)
Absorbance/ 1000-3050 cm-1
1st Dev, 2nd Dev, SNV, MSC, OSC, SavitzkyGolay 1st Dev, 2nd Dev, Wavelet compression
PLS
PLS
R2p=0.83, RMSEP=0.18 R2p=0.98, RMSEP=0.11
(dos Santos, Páscoa, Porto, Cerdeira, & Lopes, 2016)
PLS
R2cal=0.71-0.98, RMSEC=0.02-208.08 R2val=0.54-0.92, RMSEP=0.02-312.43
(Ozturk, Yucesoy, & Ozen, 2012)
Wine
Total phenol (mg/L), Anthocyanin (mg/L), brix (%), titratable acidity (g/L), pH, color intensity,
Raki
Total phenol (mg/L), Electrical conductivity (μS/cm), sugar (ppm), pH, Brix (%)
Chinese rice win
Alcohol degree (%), total sugar (g/L), non-sugar solid (g/L), total acid (g/L), pH Alcohol (%), specific gravity, pH, titratable acidity (g/L), volatile acidity (g/L), glucose + fructose (g/L) Total sugar(g/L), nonsugar solid (g/L), glucose (g/L), Isomaltose (g/L), Isomaltotriose (mg/L), Maltose (g/L), panose (g/L), Total acid (g/L), amino acid nitrogen (g/L), pH, Lactic acid (g/L) Total phenolics (mg L-1 gallic acid), total anthocyanins (mg L-1 malvidin-3-glucoside), Condensed tannins (mg L-1 catechin) Ethanol (%), Sucrose (%), Tartaric acid (%)
Red & white wine
Chinese rice wine
Red wine
Gin, rum, vodka, etc.
650-4000 cm-1
650-4000 cm-1
1st Dev, 2nd Dev, Wavelet compression
400-4000 cm-1
4000-375 cm-1
r2= 99.4 RMSECV=32.4, r2=98.2, RMSECV=10.4 r2=97.4-94.1, RMSECV=1.65-10.7 r2=97.1, RMSECV=24.3 r2=97.2, RMSECV=0.37 R2=0.9614, RMCECV=1.37, R2=0.9594, RMSEP= 1.68 R2=0.9548, RMCECV=0.02, R2=0.9434, RMSEP= 0.03 R2=0.9311, RMCECV=0.13, R2=0.9195, RMSEP=0.11 Calibration and cross validation range rcal=0.96-0.99, RMSEC=0.39-226, rcv=0.91-0.99, RMSECV=0.42-245,
oo
Chloride (g L-1) Sulfate (g L-1)
R2=0.97, RMSECV=1.93, R2=0.96, RMSEP=2.07 R2=0.97, RMSECV=2.64, R2=0.97, RMSEP=2.35 R2=0.95, RMSECV=0.08, R2=0.93, RMSEP=0.09 R2=0.96, RMSECV=0.01, R2=0.95, RMSEP=0.02 R2=0.999, RMSECV=0.0008, R2=0.999, RMSEP=0.1
-p r
Wine
PLS
re
Makgeolli wine
1st Dev, 2nd Dev, SNV, MSC, Savitzky-Golay
lP
Red wine
4000-400 cm-1
(Wu, Xu, Long, Zhang, et al., 2015)
(Debebe, Redi-Abshiro, & Chandravanshi, 2017)
(Anjos, Santos, Estevinho, & Caldeira, 2016)
(Wu, Long, et al., 2015)
(Di Egidio, et al., 2010)
PLS
PLS
R2cal= 0.99-0.99, RMSEC=0.07-523.60 R2val=0.76-0.98, RMSEP=0.10-464.70
(Ozturk, et al., 2012)
PLSR, SMLR, ANN
PLSR
R2c=0.817-0.978, RMSECV=0.133-3.310 R2v=0.789-0.968, RMSEP=0.076-2.740
(Shen, Wu, Wei, Liu, & Tang, 2017)
-----
PLS
PLS
R2=0.85-0.96, SECV=0.001-1.40 R=0.65-0.99, SEP=0.0007-1.35
(D Cozzolino, W Cynkar, N Shah, & P Smith, 2011b)
na
Chinese rice wine
156/117:39 HPLC, 3,5-dinitrosalicylic acid colorimetry method
1st Dev, 2nd Dev, Smoothing, MSC, SNV
ur
Ethiopian traditional alcoholic beverages Grapederived spirits
TE/L) Total sugar (g/L), Ethanol (g/L), Total acid (g/L), Amino nitrogen (g/L)
Jo
Chinese rice wine
90/41-66:15-20 HPLC, Chinese National Standard GB 13662–2008
Transmission/ 950-3300 cm-1
-----
PLSR
PLSR
rcal=0.821-0.991, RMSECV=0.046-50.6 rval=0.488-0.990, RMSEP=0.029-36.3
(Shen, Ying, Li, Zheng, & Hu, 2011)
600/400:200 UV-vis Spectroscopy
Transmission/ 979-2989 cm-1
-----
PLS
PLS
(Fragoso, Acena, Guasch, Mestres, & Busto, 2011)
100 Synthetic sample
600–4000 cm-1
-----
PLS
PLS
PLS
PLS
R2cal>0.95, R2val>0.95 based on selected region R2cal>0.93, R2val>0.91 based on selected region R2cal>0.82, R2val>0.72 based on selected region R2=0.9910, RMSECV=0.20, R2=0.9896, RMSEP=0.21 R2=0.9962, RMSECV=0.10, R2=0.9956, RMSEP=0.11 R2=0.9898, RMSECV=0.16, R2=0.9888, RMSEP=0.16 rcal=0.979, RMSEC=0.40, rval=0.949, RMSEC=0.50
PLS
PLS
R2=0.940-0.999, SECV=0.0007-40.8 R2=0.901-0.981, SEP=0.0013-47.30
(Lachenmeier, 2007)
R2cal>0.93, RMSEC=0.6-1090.0, RMSECV=16.7-2950.0, RMSEP=0.2-139.0 R2cal>0.85, RMSEC=0.0-0.7, RMSECV=0.2-1.9, RMSEP=0.0-0.9 R2cal=0.8167-0.9835, RMSEC=3.92-119.03
(Preserova, Ranc, Milde, Kubistova, & Stavek, 2015)
Beer
Attenuation limit (%)
40/30:10 European brewery convention method
4000-600 cm-1
Beer, spirit drinks
Telative density, alcohol (%), original gravity, pH, lactic acid (mg/L), bitterness unit, EBC color, quotient after weber, extinction after weber
926-5012 cm-1
Red wine, rose wine, white wine
Total phenolic compound, Total antioxidant activity
535 spirit drinks 461 beers/ Oscillation-type Densimetry, Refractometry, gas chromatography, Enzymatic analysis, Photometry 25/ UV-visible Spectrophotometer,
1st Dev, 2nd Dev, SNV, MSC, Baseline correction -----
4000-600 cm-1
1st Dev, 2nd Dev,
PLS
PLS
Chinese
17 Free amino acid
116/87:29
4000-400 cm-1
1st Dev, 2nd Dev,
PLS, SiPLS,
GA-
(Nagarajan, Gupta, Mehrotra, & Bajaj, 2006)
(Castritius, Geier, Jochims, Stahl, & Harms, 2012)
(Wu, Xu, Long, Wang, et al.,
GA-SiPLS
SiPLS
R2pre=0.8074-0.9744, RMSEP=4.21-130.56
2015b)
PCA
PCA
PC1, PC2 and PC3 explaining 99.8%total variance between samples
(Garcia-Hernandez, et al., 2020)
SNV, SavitzkyGolay, Smoothing, 2nd Dev
PCA, LDA
LDA
Overall classification rate 93%
(Yadav & Sharma, 2019)
PCA, PLS-DA
PLS-DA
(Gordon, et al., 2018)
Transmission/ 4000-400 cm-1
-----
Model 1-4, Model 5-8
Model 2 Model 8
Correct classification 100% for commercial & craft Correct classification 100% for lager Correct classification 98% for dark & pale ale Discrimination rate 100% per origin Discrimination rate 99.0% per time of maturation
156/117:36
Transmission/ 4000-800 cm-1
1st Dev, 2nd Dev, SNV, MSC
PCA, PLS-DA
PLS-DA
Calibration=94.0%, Prediction=94.9%
(Wu, Long, et al., 2015)
98
Transmission/ 400-4000 cm-1
PCA, LDA, SIMCA
SIMCA
Overall correct classification rate 93.02%
(Riovanto, et al., 2011)
75
700-4000 cm-1
PCA, LDA
LDA
Correctly classified 100% in calibration and prediction set
(Di Egidio, et al., 2010)
496
Transmittance/ 5011-929 cm-1
2nd Dev, SNV, Savitzky-Golay derivation & smoothing SNV, MSC, Derivative transformation -----
PCA, LDA
LDA
(Louw, et al., 2009)
17 whisky & 14 caramel colorants
600-1800 cm-1
150/15:135
400-4000 cm-1
Correct classification 98.3% with MIR spectra (red cultivar), whereas, combination of spectra and volatile compounds correctly classify 86.8% (white cultivar) Correctly differentiate between different caramel colorants and whisky samples Prediction accuracy >97.0%
151/85:43:23
Transmission/ 4000-400 cm-1
172
Transmission/ 400-4000 cm-1
36
Transmittance/ 4000-400 cm-1
75
600-3000 cm-1
48
4000-400 cm-1
Discrimination and authentication of whiskies from Scotland, Ireland & USA based on origin & time of maturation (2-12 year) Fermentation stage classification (0-3, 4-6, 7-9, & 10-20 days Four Western Australia and Coonawarra were separated from other wines Fermentation monitoring (0, 2, 5,7 and 30–35 days) Characterization of young white & red wine cultivar using MIR and GC-MS Detection of counterfeit samples for authenticity
100 Scottish, 50 Irish % USA/25% validation
Detection of lethal fake liquors Discrimination of Cognacs and other distilled drinks (armagnacs, whiskies, brandies, bourbons, rums, and counterfeit products) Classify commercial wines sourced from organic & non-organic production system
Beers
Whisky
Chinese rice wine Shiraz wines
Red wine
South African wine Scotch whisky Liquors Cognacs & noncognacs drinks
IDWT, -----
PCA
PCA
PCA, LS-SVM
LS-SVM
PCA, PLS-DA
PLS-DA
Correctly classified 95% of samples in test set
-----
PCA, DPLS, LDA
DPLS
Classification rate for organic (100%) and non-organic (88%) for white wine Classification rate for organic (73%) and non-organic (85%) for red wine
ur
na
Organic and nonOrganic wines
-----
lP
Illicit liquors
oo
Discriminate wines according to their phenolic content Classification of geographical origin of India Confirm their identity (e.g. ale vs lager, commercial vs craft beer)
-p r
Red wines
(Sujka & Koczoń, 2018)
(McIntyre, Bilyk, Nordon, Colquhoun, & Littlejohn, 2011) (D. Chen, Tan, Huang, Lv, & Li, 2019) (Picque, et al., 2006)
re
(mg/L)
f
SNV, MSC, Savitzky-Golay de-trending, -----
rice wine
(Cozzolino, Holdstock, Dambergs, Cynkar, & Smith, 2009)
Table 3. Application of Raman spectroscopy for quantitation, discrimination, and authentication of alcoholic beverages Parameters
No of sample/division /Quantification
Wavelength range/Laser
White wine
Alcoholic strength (%), total sugars (g/L), total acidity (g/L), volatile acidity (g/L), pH, Density (g/mL)
3500-57 cm-1 1064 nm laser source
1st Dev, 2nd Dev, SNV, Savitzky– Golay, Normalization
Chinese rice wine
Ethanol content (g/L), Total sugar (g/L), Total acid (g/L), Ph
200-2000 cm-1
Chinese rice wine
Ethanol content (%)
108/75:33 Distillation & densimetry, enzymatic method with spectrophotometry, Densimetry, Potentiometric titration, Potentiometry 112/83:28 HPLC, 3,5Dinitrosalicylic acid colorimetry, titration pH meter 30/ Gravimeter method
Chinese rice wine
Ethanol (g/L) Glucose (g/L)
156/117:39 HPLC
200-3000 cm-1 785 nm laser source
1st Dev, 2nd Dev, SNV, MSC, Baseline correction, Detrending Calibration and background removal was done using OMINC software 1st Dev, SNV, MSC, de-trending, Smoothing, Savitzky–Golay
Wine
Alcohol (v/v) Sugar (g/L)
40/ HPLC
4000-400 cm-1
Red wine
Polyphenols (mg/lt), Anthocyanins (mg/lt), Tannins (mg/lt)
114/76:38 Spectrophotometer,
0-2500 cm-1
Wine
Sugar (g/L), Ethanol (%), Glycerol (%)
85/35:50 HPLC
400-4000 cm-1
Apple
Polyphenolic content (mg
Spectrophotometer,
400-4000 cm-1
Jo
Beverage type
50-3350 cm-1 785 nm laser excitation
Pretreatment
1st Dev, baseline, SNV, MSC, DOSC, Savitzky– Golay Average & dark subtraction, Savitzky–Golay, Mean centering, Rolling circle SNV, MSC, Smoothing, Baseline correction Savitzky–Golay, Normalized
Chemome trics
Optimal model
Precision
Reference
PLS
R2p=0.592-0.967, RMSECV=0.0005-0.912, RMSEP=0.0005-0.845, RMSEC=0.0004-0.71
(dos Santos, et al., 2018)
PLS, SiPLS
SiPLS
R2=0.97, RMSECV=1.88, R2=0.95, RMSEP=2.40 R2=0.91, RMSECV=1.39, R2=0.90, RMSEP=1.54 R2=0.94, RMSECV=0.17, R2=0.93, RMSEP=0.19 R2=0.95, RMSECV=0.04, R2=0.93, RMSEP=0.05
(Wu, Long, et al., 2016)
PLS
PLS
R2=0.9258, RMSEC=0.434, RMSEP=0.514
(Yang & Ying, 2011)
PLS, CARSPLS, SVM, CARSSVM PLS
CARSSVM
R2=0.97, RMSECV=3.93, R2=0.97, RMSEP=4.07 R2=0.97, RMSECV=2.75, R2=0.97, RMSEP=2.90
(Wu, Xu, Long, Wang, et al., 2015a)
PLS
r2=0.9982, RMSECV=0.0042, RMSEP= 0.0036 r2=0.9986, RMSECV=0.0030, RMSEP=0.0032
(Q. Wang, Li, Ma, & Si, 2015)
PLS
PLS
(Gallego, Guesalaga, Bordeu, & González, 2010)
PCA, PLS
PCA-PLS
Rc2=0.982, RMSEC=1.81, Rv2=0.829, RMSEV=5.55 Rc2=0.991, RMSEC=8.86, Rv2=0.844, RMSEV=38.5 Rc2=0.995, RMSEC=1.76, Rv2=0.895, RMSEV=8.65 R2=0.995, RMSECV=0.17, RMSEP=0.22 R2=0.999, RMSECV=0.09, RMSEP=0.03 R2=0.980, RMSECV=0.40, RMSEP=0.20
PLS-1
PLS-1
R2C=0.9062, RMSEC=42.4, R2P=0.7933,
(Śliwińska, et al.,
PLS
(Q. Wang, Li, Ma, & Liang, 2014)
GA/L), density (g/cm3)
Density meter
Wine
Classification of wines, from two sorts; Feteasca Regala & Sauvignon Blanc, produced in three Romanian viticulture regions, during five consecutive vintages Discrimination of fermentation stages
30
-1000-3600 cm-1
156/117:39
200-3000 cm-1 785 nm laser source
Wine
Discrimination of lactic acid bacteria
456
3400–200 cm-1 532 nm laser emission
Chinese rice wine
Classification to predict the different wineries
112/83:28
200-2000 cm-1
Malt Scotch whisky
Counterfeit & adulterate discrimination
44
200-3500 cm-1 785 nm laser excitation
Tequilas
Discrimination of silver and aged tequilas
15
450-4000 cm-1
Mezcal
Discriminate between mezcal samples with different aging time
33
200-1800 cm-1 785nm laser source
2016)
(Magdas, Guyon, Feher, & Pinzaru, 2018)
-----
SLDA
SLDA
1st Dev, SNV, MSC, de-trending, Smoothing, Savitzky-Golay Polynomial Subtraction, SNV
PCA, DPLS
DPLS
Correct classification 94.9% in both calibration & prediction set
(Wu, Xu, Long, Wang, et al., 2015a)
PCA, SVM
SVM
Species of Pediococcus & Lactobacillus; & strains of O. oeniand P. damnosuswere were classified with high sensitivity between 86-90 and 84-85%, respectively
(Rodriguez, Thornton, & Thornton, 2017)
PCA, SIMCA, LDA
LDA
Correct classification 98.82% in calibration set Correct classification 100% in prediction set
(Wu, Long, et al., 2016)
PCA, PLSR
PCA, PLSR
(Kiefer & Cromwell, 2017)
PCA
PCA
PCA was sensitive to the cask type in which a whisky was matured and/or finished PLS distinguish between whiskies containing natural colors only or with artificial colorants, age difference, alcohol content & processing Total variance represented by first three PCs were 80%, 10% & 9%, respectively for discrimination
1st Dev, 2nd Dev, SNV, MSC, Baseline correction, Detrending -----
Normalization
oo
-----
PCA, PLSDA
PCA, PLS-DA
Matured mezcal with different aging time was successfully classified
-p r
Chinese rice wine
RMSEP=67.5 R2C=0.9623, RMSEC=0.00, R2P=0.8566, RMSEP=0.00 Discrimination rate of wine variety and geographical origin of 100% in both initial and cross-validation was achieved
f
liqueurs
(Frausto-Reyes, Medina-Gutiérrez, Sato-Berrú, & Sahagún, 2005) (Elías, et al., 2015)
Table 4. Application of e-tongue for quantitation, discrimination, and authentication of alcoholic beverages
TPI280 Folin-Ciocalteu index glories color parameters CIELab color parameters Ferulic acid (µM), Gallic acid (µM), Sinapic acid (µM)
Beer
94/71:23
Lager beer
Alcohol strength (% vol) Color (EBC unitcolor)
Cava wine
Total sugar (g L-1) Total dry extract (g L-1)
Rice wine
Age prediction (one, three, & five year) Prediction of flavor, Marked age, Total soluble solid (%) Prediction of geographical origin Polyphenolic content (mg GA/L), Density (g/cm3) Real extract (Plato◦), Alcohol volume (%), Bitterness (EBU), Polyphenols (mg/L),
25 UV-Vis Spectrophotometer EBC=25׃×A430 63/48:15 FTIR with wine Scan (sugar), from Specific density and alcohol (dry extract) 120/96:24
Apple liqueurs Beer
-1000 to 1000 mV
-----
-----
Windowed slicing integral method, pruning step -----
ANN
ANN
ANN
ANN
r=0.995, RMSE=0.21, r=0.999, RMSE=0.13
Bell-shaped windowing, Compression method Fast Fourier transform
PLS
PLS
R2=0.9751 R2=0.9869
ANNs
ANNs
r=0.992, r=0.952 r=0.991, r=0.938
PLS, BPANN PLSR, SVM-LOO, SVM-TF ELM
PLS, BPANN PLSR, SVMLOO, ELM
R2=0.999, RS=0.004 (PLS) R2=0.999, RS=0.007 (BP-ANN) R2=0.9952 (PLSR), R2=0.9568 (SVM-LOO), R2>0.95 (SVM-LOO) R2=0.9647, RMSE=0.26, R2=0.9436, RMSE=0.36
PLS-1
PLS-1
R2C=0.9767, RMSEC=29.75, R2P=0.9396, RMSEP=47.74 R2C=0.9252, RMSEC=0.001, R2P=0.8783, RMSEP= 0.001
-----
-0.6 to 1 V
ur
Alcohol content
Rice wine
Pretreatment
98/75:23
Beer
Rice wines
Potential
-1.0 to +1.3 V
Jo 275/150:125
Chemomet rics
0 to 0.5 V -----
200
0 to 1.5 V
Spectrophotometer, Density meter
-1.0 to +1.0 V
Optimal model
re
Red wines
No of sample/ division/ Quantification 36/ Spectrophotometer, Color meter
PLS-1
lP
Parameters
na
Beverage type
---------
----Normalized
PLS-1
(Cetó, GutiérrezCapitán, Calvo, & del Valle, 2013) (Blanco, De la Fuente, Caballero, & Rodríguez-Méndez, 2015) (Cetó, Capdevila, Puig Pujol, & del Valle, 2014)
(Zhenbo Wei, Wang, & Ye, 2011) (ZhenBo Wei, Wang, Cui, & Wang, 2016) (J. Wang, Zhu, Zhang, & Wei, 2019) (Śliwińska, et al., 2016)
PLS
PLS
R=0.90, RMSEC=0.60, R=0.76, RMSEP=0.78, R=0.85, RMSEC=0.88, R=0.70, RMSEP=1.10, R=0.93, RMSEC=2.10, R=0.89, RMSEP=2.50, R=0.83, RMSEC=46.0, R=0.81, RMSEP=52.0,
(Polshin, et al., 2010)
-----
-----
PLSR
PLSR
r2>0.31, RMSEP<0.52 r2>0.61, RMSEP<79.04 r2>0.48, RMSEP<327.06 r2>0.54, RMSEP<37.20
(Kang, Lee, & Park, 2014)
PLS
PLS
(Cetó, et al., 2017)
PLS
PLS
R=0.982, NRMSE=0.048, r=0.969, NRMSE=0.077 R=0.981, NRMSE=0.045, r=0.917, NRMSE=0.110 R2>0.742 (training set) R2>0.646 (test set)
R=0.965, RMSE=15.1, r=0.896, RMSE=26.6 R=0.977, RMSE=0.55, r=0.858, RMSE=1.52 R=0.895, RMSE=0.49, r=0.791, RMSE=0.74 R=0.993, RMSE=9.50, r=0.886, RMSE=41.4 R=0.940, RMSE=2.36, r=0.755, RMSE=4.89 PC1, PC2 and PC3 explaining 85.8% total variance between samples Correct classifications from individual samples was
(Cetó, Capdevila, Mínguez, & del Valle, 2014)
Wine
Ageing time, Global sensory scores (0-10) Process duration, ethyl alcohol content, density, extract content, Content of reducing sugars Folin index (mg L-1), I280 index (arb. unit), I320 index (arb. unit), Tannins (mg L-1), Anthocyanins (mg L-1) Discriminate wines according to their phenolic content Märzenbier style, Lager,
52
-1.0 to +1.3 V
33
-0.5 to 1.5 V
Fast Fourier transform, GAs -----
20/ Acid butanol assay, UV/VIS Spectrophotometer
-0.4 to 0.8 V
-----
PLS1, ANN
ANN
36
-1000 to 1000 mV -----
-----
PCA
PCA
LDA
LDA
Beer
(Cetó & del Valle, 2014)
-----
9×3 batches Sensory evaluation HPLC, LC–MS/MS
Red wines
(Garcia-Hernandez, et al., 2020)
-----
Sensory attributes, 3-sugars (mg/L), 5-organic acids (mg/L), 9-amino acids (mg/L)
Rosé cava wines
Rc2=0.9343, RMSEc=2.01, Rp2=0.8956, RMSEp=2.60 Rc2=0.9276, RMSEc=1.89, Rp2=0.8944, RMSEp=2.34 Rc2>0.97, RMSEc<0.53, Rp2>0.94, RMSEp<0.86 Rc2>0.97, RMSEc<0.92, Rp2>0.94, RMSEp<1.38 r=0.994, r=0.977, r=0.986 r=0.998, r=0.988, r=0.981 r=0.994, r=0.978, r=0.976
Reference
119/79:40
Korean rice wines
Wine
Precision
94
Windowed
(Kutyła Olesiuk, Wesoły, & Wróblewski, 2018)
(Garcia-Hernandez, et al., 2020) (Cetó & del Valle,
Pilsen, Black beer, low alcohol & Alsacien style beer discrimination Classification of geographical origin
Rice wine
200
0 to 1.5 V
slicing integral method, pruning step -----
-----
-----
81.9%
2014)
PCA, LLP, LDA
LLP,
Successfully classified geographical origin with total variance of 99.81% (LLP1 89.88%, LLP2 9.93%),
(J. Wang, et al., 2019)
PCA, LDA
LDA
(Cetó, et al., 2013)
PCA, LDA
LDA
LDA explained variance for first two discrimination function was 94.4% and correct classification rate was 81.9% Correct classification rate according to beer style was 100%
PCA, LDA
LDA
Correct classification rate 100%
Beer
Discrimination of different commercial beer types
51
Lager beer
Discrimination of various types of beer
25
-0.6 to 1 V
Cava wine
Classification based on different ageing times
65
-1.0 to +1.3 V
Italian barbera wines Chinese rice wine
Classify four types of barbera wines
53
-----
-----
PCA, LDA, CART
LDA
Correct classification rate 98.1%
Discriminate the samples from different vintage years based on 28 taste-active compounds and the sensory attributes Differentiate chinese rice wine of different marked ages (one, three, & five year) Classify according to four different marked ages (3, 5, 8 and 10 years) for authentication
32 Sensor evaluation, HPLC
-----
-----
LDA, PLSDA
LDA, PLS-DA
Build model successfully discriminate the sample sourced from different vintage year (2003, 2005, 2008, & 2010)
(Cetó, Capdevila, Puig Pujol, et al., 2014) (Buratti, Benedetti, Scampicchio, & Pangerod, 2004) (Yu, Zhao, Li, Tian, & Ma, 2015)
120
0 to 0.5 V
-----
PCA, CA
PCA
Configuration of location of each sample was clear in PCA score plot with total variance of 96.92%
(Zhenbo Wei, et al., 2011)
120/80:40
-0.5 to 1.6 V
Normalization,
BPANN
Classification rate was 100% in calibration & prediction set
(Ouyang, Zhao, & Chen, 2013)
Beer
Identification of three style of beer (lager, stout, & IPA)
25
-1.0 to 1.2 V
Discrete wavelet transforms
SIMCA, PLS-DA, KNN, BPANN, SVM LDA
Chinese rice wine Rice wines
Discrimination of seven classes Discrimination of three type of wine Brand uniformity control (Primitivo & Negroamaro)
72/51:21
-----
-----
165
-----
-----
Discrimination & identification of wine Discrimination of the effect of the barrel in wine maturing & ageing time Qualitative assessment of wine production process Classification of wine samples according to aging time Discrimination between dark & pale beer
1599 red wine 4898 white wine 52
-1.0 to +1.3 V
33
-0.5 to 1.5 V
78
+1.01 to +1.31 V
Discriminate between expensive & cheap whisky samples; & to detect adulteration processes Differentiation of fermentation time
12
Wine Cava wine
Beer
Wines & whiskies
Pomegranate wine
21
DFA worked better in classifying three types of wine
PCA, SIMCA, PLS-DA PCA, ANN
PLS-DA
Correct discrimination of more than 70%
ANN
Classification and identification rate 98%
Fast Fourier transform, GAs
PCA, LDA
LDA
Classification rate 100% of the type of barred used during maturation process
-----
PCA, PLSDA LDA
PCA, PLS-DA LDA
Higher rate of classification was observed using hybrid electronic tongue Correct classification between 80 to 96%
(Kutyła Olesiuk, et al., 2018) (Giménez-Gómez, et al., 2016)
-----
PCA, CA
PCA
Clear discrimination between dark & pale beer was obtained
-----
PCA
PCA
Successful discrimination was observed
(Arrieta, RodríguezMéndez, De Saja, Blanco, & Nimubona, 2010) (Novakowski, Bertotti, & Paixão, 2011)
-----
PCA
PCA
Good discrimination was observed with PC1 (82.14%) & PC2 (11.66%) variance
-----
Normalized
Autoscaling
-1.0 to 0.5 V
-0.9 to 0.8 V
Sample prepared at 0, 2, 4, 6, 8, 10, 18, & 26 days
f DFA,
-0.3 to 1.5 V
-----
oo
Average prediction accuracy 97.22%
-p r
ANN
re
Wine
DFA, PCA, ANN PCA, DFA,
lP
Wine
24
Qualitative classification rate was 100% with mid-level fusion of sensors
na
Apulian red wines
(Blanco, et al., 2015)
LDA
ur
Chinese rice wine
Jo
Rice wine
Bell-shaped windowing, Compression method Fast Fourier transform
-----
(J. Gutiérrez, MorenoBarón, Cetó, Mimendia, & del Valle, 2012) (Lu & Cai, 2014) (ZhenBo Wei, et al., 2016) (Lvova, et al., 2018)
(Díaz & Acevedo, 2014) (Cetó, et al., 2017)
(Lan, et al., 2017)
Table 5. Application of e-nose for quantitation, discrimination, and authentication of alcoholic beverages Beverage type
Parameters
Apulian wines Red wine
18 Volatile chemical compounds prediction 4-ethylphenol (µg L-1), 4-ethylguaiacol (µg L-1) Predict wine quality parameters & sensory descriptors Aging time (months)
No of sample/ division/ Quantification 17 SPE/GC-MS 46 GC-MS 28 GC-MS, Sensory panel 20×3=60
4-ethylphenol (µg L-1)
213
Rice wine
Marked age (years)
250/150:100
Liquors
25 GC-MS
Apulian wines
Characterization of famous liquors based on their flavor & origin Discrimination according to different grape variety
17
Chinese rice wine Chinese spirits
Classification of wine according to their age Classification of spirits according to their flavor
Beer
Aging fingerprint characterization for
42
Wine
Sugar cane spirits Red wine
Pretreatment
Chemometrics
Optimal model
Precision
Reference
PLS, RSR
RSR
R2=0.418-0.950, RMSEP=0.05-15
(Capone, et al., 2013)
PLS
PLS
PLS, PCA
PLS, PCA
r2=0.91, RMSECV=118.4 r2=0.89, RMSECV=20.4 Better results were observed for sensory score prediction than the quality parameters
(Berna, Trowell, Cynkar, & Cozzolino, 2008) (Jesus Lozano, et al., 2007)
Logarithmic transformation SNV
PLS
PLS
R=0.99, RMSE=0.6, r=0.98, RMSE=1.2
PLS
PLS
Normalization, Feature extraction -----
PLSR, SVM
SVM
R2cal=0.70, RMSECV=142.8 (barrel) R2cal=0.90, RMSECV=75.7 (bottle) R2=0.9942, RMSE=0.0404
(Martí, Pino, Boqué, Busto, & Guasch, 2005) (Cynkar, Cozzolino, Dambergs, Janik, & Gishen, 2007) (Zhebo Wei, Xiao, Wang, & Wang, 2017)
PCA, HCA
PCA, HCA
Higher differentiation was observed for both chemometrics
(Xiao, et al., 2014)
-----
PCA
PCA
(Capone, et al., 2013)
32
-----
PCA, DA
DA
Negroamaro & Primitivo wines were successfully classified, PC1 (67.50%), PC2(23.59%) Correct classification rate was 96.88%
144/96:48
-----
PCA, CA, SVM, LDA, BP-ANN PCA, LDA, PNN, RBF, BP
SVM
Training and testing accuracy were 91.6 & 83.3%, respectively
(M. Liu, et al., 2012)
BP
Classification accuracy for aged alcoholic beer was 90.47%
(Ghasemi-Varnamkhasti, et al., 2011)
----SNV, feature extraction Normalized
Autoscaling
(Yu, Dai, Yao, & Xiao, 2014)
Autoscaling
66 Datasets I 120 Datasets II 90 Datasets III
Vector normalization, Feature extraction
Wine
Pomegranate wine Spanish wine Red wine
Classification of five wine types Monitor spoilage induced by Brettanomyces yeast
Beers
Sauvignon blanc wines Beer, wines & spirits
SLDA
Classified correctly 86% of the samples
(Cynkar, Dambergs, Smith, & Cozzolino, 2010)
Discriminate correctly with a minimum accuracy of 97.2% Successfully discriminate the control & off-flavor doped-wines
(J Lozano, Santos, & Horrillo, 2005) (Ragazzo-Sanchez, Chalier, & Ghommidh, 2005)
Good discrimination was observed with PC1 (65%) & PC2 (19%) variance Successfully discrimination was observed between samples
(García, et al., 2006)
Autoscaling, Normalization -----
PCA, PNNs
PNNs
48
-----
PCA
PCA
PCA, PNN
PCA
PCA
PCA
PCA, CDA
CDA
PCA, CA
Centering, scaling Normalization
9
48
-----
(García-Martínez, et al., 2011)
Good discrimination between control & treated sample was observed
(Martin, Mallikarjunan, & Zoecklein, 2008)
Excellent discrimination was observed
(Cabañes, Sahgal, Bragulat, & Magan, 2009)
oo
20
f
9 Flavors
5×2 strain
Normalization
PCA, CA
17
Centering, scaled, feature extraction -----
PCA, ANNs, PNN
ANNs
Classification success rate was 97%
(J Lozano, Arroyo, Santos, Cabellos, & Horrillo, 2008)
PCA
PCA
Good discrimination was observed with PC1 (86.38%) & PC2 (8.30%) variance
(Lan, et al., 2017)
Normalization
PCA
PCA
Excellent discrimination was observed with PC1 (83.8%), & PC2 (7.50%) variance Correctly classified 67% of sample, further improvement needed improve the calibration accuracy & robustness. Classification accuracy was 100% in both training & test set
(Bellincontro, García-Martínez, Mencarelli, & Moreno, 2013) (Cynkar, et al., 2007)
Higher misclassification rate was observed
Sample prepared at 0, 2, 4, 6, 8, 10, 18, & 26 days 5 types
Discrimination of geographical origin Identification & classification of different alcoholic beverages Identification with different marked ages
34
Identification & discrimination of wine aroma Off-flavors detection in alcoholic beverages
16
Whiskey
Detection of methanol contamination
Wine
Classification of various kings of wines Effect of temperature on the fingerprint of wines Classification of wines by their varietal origin
1, 5, 10, 20% Methanol contamination 4 Types of wines
SLDA
PCA, LDA, SIMCA, PLSDA, SVM
LDA, SIMCA, PLS-DA, SVM LDA
LDA
-----
PCA, DFA
PCA, DFA
Allowed to clearly visualize the differences among the classes
PCA, LLE, LDA
LDA
Best identification rate was observed
(Berna, Trowell, Clifford, Cynkar, & Cozzolino, 2009) (Ragazzo-Sanchez, Chalier, Chevalier, Calderon-Santoyo, & Ghommidh, 2008) (Zhebo Wei, et al., 2017)
PCA, PNN, ANN
ANN
Success rate in classification was 100% in white wine & 98% in red wine
(Jesús Lozano, Santos, Sayago, Gutierrez, & Horrillo, 2004)
PCA, DFA
PCA, DFA
Clear visualization among brands or origins were observed
Feature extraction
PCA
PCA
Perfect classification between pure & methanol contamination
Centering, Scaling Autoscaling, Smoothing, Normalization Normalization
PCA, PNN
PCA, PNN PLS-DA
Classification success rate 100% with polysilicon heater Correct classification rate ranged between 86-100% for 60, 70, 75, 80, & 85 ˚C
(Ragazzo-Sanchez, Chalier, Chevalier-Lucia, CalderonSantoyo, & Ghommidh, 2009) (Wongchoosuk, Wisitsoraat, Tuantranont, & Kerdcharoen, 2010) (Aleixandre, et al., 2008)
DPLS
Correct classification rate >90% for both varieties
Jo
Normalization, Feature extraction -----
40 (beer) 48 (wine)
15 white variety, 15 red variety 150
(Ghasemi-Varnamkhasti, Mohtasebi, Siadat, Ahmadi, & Razavi, 2015)
-----
ur
24 (beer) 72 (wine) 72 (spirit) 250
PCA, SLDA
na
Feature extraction, Autoscaling
Australian white wines
(Wongchoosuk, Choopun, Tuantranont, & Kerdcharoen, 2009) (Vera, et al., 2011)
Complete characterization of beer was produced
Beer-2 types 4-alcoholic 2-non alcoholic
Wine
(Fu, Li, Qin, & Freeman, 2007)
Fisher LDA
Discrimination of beers
Beer & wine
Average classification correction rates for Datasets I, II and III were 97.5%, 93.9% and 61.6%, respectively
PCA, Bayesian LDA, SLDA, Fisher LDA PCA, PLS-DA, SLDA
SNV
Wine
(Santos, et al., 2004)
-----
213
Rice wine
Overall classification success 88.3%
Clear discrimination was observed with PC1 & PC2 variance of 80.60 & 16.29%, respectively.
60
Wine
PCA, PNN KIII
PCA
Classification based on geographical origin (Australia, & Spain) Discrimination of added aromatic compounds Off-flavor characterization of wine using dehydration & desalcoholization step Differentiation of different red wines Differentiation of wines partially fermented by two osmo-ethanol-tolerant Yeasts Discrimination of wine with different pre-harvest & post-bloom spray treatments Discriminate of two fungal species responsible for contamination Recognition & detection of wine aging for control of fraud Differentiation of fermentation time
Wine
(Buratti, et al., 2004)
PCA
Tempranillo wine
Sweet wines
Correct classification rate 100%
-----
67
Red wine
KIII, BP-ANN, NPA
LDA
30
Discrimination of beer based on aroma profile
Wine
PCA, LDA, CART PCA, PNN
59
Beer
White wine
-----
-p r
Wine
53
re
Chinese rice wine
alcoholic beers Classify four types of barbera wines Characterization of red & white wine Discriminate six typical VOCs (ethanol, acetic acid, acetaldehyde, ethyl acetate, lactic acid and isoamyl alcohol) Discrimination of red and white wine
lP
Italian barbera wine Madrid wine
-----
PCA, PLS-DA
PCA, DPLS, LDA
(Cozzolino, Cynkar, Dambergs, & Smith, 2010) (Cozzolino, Smyth, Cynkar, Dambergs, & Gishen, 2005)
Table 6. Data fusion approach for quantitation, discrimination, and authentication of alcoholic beverages Beverage type
Parameters
Chinese rice wine
DPPH (mg TEAC/L) ABTS (mg TEAC/L) FRAP (mg TEAC/L) TPC (mg GAE/L)
Wine
Oxygen related parameters (mg L-1) Polyphenols (mg L-1) Colour indices (mg L-1) Original extract (%) Real extract (%) Ethanol (%) Sensorial descriptors and
Beer
No of sample/division /sensor used 111/83:28 ATR-IR, Raman
Wavelength range 4000-400 cm-1 200-2000 cm-1
16 e-nose, e-tongue
43/15:28 NIR, MIR 15
Chemometrics
Optimal model
1st Dev, 2nd Dev, MSC, SNV, Baseline, detrending
PLS, SiPLS, SiPLSC, SiSVMC
SiSVMC
-1.0 to 1.3 V (etongue)
Kernel functions
PLS1
PLS1
800-2857 nm, 4000-600 cm-1
-----
PLS, ANN
ANN
-----
Gas
Gas
-----
Pretreatment
Precision R2c=0.9387, RMSECV=5.63, R2p=0.9331, RMSEP=5.89 R2c=0.9451, RMSECV=3.52, R2p=0.9401, RMSEP=3.83 R2c=0.9686, RMSECV=11.22, R2p=0.9628, RMSEP=12.02 R2c=0.9572, RMSECV=16.59, R2p=0.9529, RMSEP=17.94 Rc>0.840, RMSECV<0.126, Rp>0.767, RMSEP<0.193 Rc>0.889, RMSECV<96.05, Rp>0.754, RMSEP<166.37 Rc>0.812, RMSECV<1.868, Rp>0.745, RMSEP<2.924 RMSEP=0.14 RMSEP=0.076 RMSEP=0.091 R2>78%
Reference
(Wu, Xu, et al., 2016)
(Rodriguez-Mendez, et al., 2014)
(Iñón, Garrigues, & de la Guardia, 2006) (Buratti, Ballabio,
Red Spanish wines
Characterization of wines
Beer
Classification of beer flavor Chemometric classification of beers
Red wine
Identification of different wine classes
(Rodríguez-Méndez, et al., 2004)
SVM, RF, ELM PCA, LDA
ELM
Prediction accuracy 98.33% achieved with fusion model
(Men, et al., 2017)
LDA
Relative higher accuracy was achieved with mid-level fusion model
(Tan, Li, & Jiang, 2015)
PCA, LDA
LDA
Correct classification rate was 96% based on the combination of potentiometric & voltammetric sensors
(J. M. Gutiérrez, et al., 2013)
PCA, SIMCA, PLS-DA
PLS-DA
Overall classification rate was 93% with NIR & MIR fusion model
(Cozzolino, et al., 2011a)
-1.0 to 1.3 V (etongue) -1.0 to 1.3 V (etongue), 780-380 nm (e-eyes)
Kernel functions
PLS-DA
PLS-DA
PCA, PLS-DA
PLS-DA
-1.0 to 1.5 V (etongue), 780 to 380 nm (optical system) -----
Autoscaling
PCA
Normalization
290-700 nm, 240-400 nm, 380–700 nm
1st Dev, Smoothing, Savitzky-Golay
-1.0 to +1.2 V
Feature extraction
400-2500 nm, 400-4000 cm-1
-----
-----
-----
Autoscaling
PCA
PCA
Clear discrimination as a function of time was achieved using sensor fusion
(Gil-Sánchez, et al., 2011)
-----
Autoscaling
PCA, PLS-DA, ICA
PLS-DA
Correct classification rate was 100%
(Di Natale, et al., 2004)
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PLS
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Beer
Relative error (2-12%) Relative error (0.6-52%) Higher correlation (>0.97) was observed for prediction of classes Rc>0.982, RMSEC<0.076, Rp>0.981, RMSEP< 0.079
Autoscaling
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Chinese lager beer
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36 e-nose, e-tongue 16 e-nose, e-tongue 16 e-nose, etongue, e-eyes 6 wines e-nose, etongue, optical system 5 beers e-nose, e-tongue 135 Fluorescence, UV & visible Spectroscopies 25 e-tongue based on multisensor 64/34:30 Vis & NIR, MIR 3 wines (1, 5, 9, 15, 19, 22, 28, 36, & days) e-nose, e-tongue 36 e-nose, Etongue
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Red wine
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Wine
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overall quality estimation
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Figures
Coupler
Double fibers
Single fibers Raman Spectroscopy
Measurement optical fiber
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Reference optical fiber
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Vessel
Filter tube
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Transparence glass
Standard
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Figure 1. Configuration of the wine fermentation monitoring system with the auto-calibration Fourier transform Raman spectroscopy. Reproduced with permission (Wang, et al. 2014) Copyright 2014 Elsevier Ltd.
Signal conditioning system PB filter
High impedance
Multiplexor + filter
High impedance
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Feature extraction
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e-nose
Normalization, SNV, FFT, Log transformation, Autoscaling etc.
Fingerprints capturing
e-tongue
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Single/Multisensor model
Qualitative model
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Quantitative model
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Wet chemical analysis
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Figure 2. Schematic diagram of e-nose and e-tongue based measurement system. Reproduced with permission (Gil-Sánchez, et al., 2011) Copyright 2011 Elsevier Ltd.
Fusion level-based spectroscopy system Low-level
Mid-level
Smartphone based CSA system Printed CSA
High-level
After exposure
Exposure to sample
Scanning
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NMR spectroscopy
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Fluorescence spectroscopy
Feature extraction
Raman spectroscopy
Spectral pretreatment
Infrared spectroscopy
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Data fusion
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Chemometrics model Discrimination or authentication model
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Figure 3. Block diagram of model development using smartphone based colorimetric sensor array (CSA) system, and fusion level based spectroscopic system.
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Figure 4. Selection and combination of optimum variables extracted from the Raman spectroscopy spectra and attenuated total reflectance infrared spectroscopy spectra. Adapted with permission (Wu, Xu, et al., 2016) Copyright 2015 Elsevier Ltd.
Highlights 1. Consumption of alcoholic beverages is an integral part of many socio-cultural traditions 2. Existing analytical methods of quality control do not fulfil the industrial requirements 3. Nondestructive techniques have great potential to substitute the traditional methods
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4. Integration of multiple sensors can generate meaningful results to deliver better output