Ultraviolet-visible spectroscopy for food quality analysis

Ultraviolet-visible spectroscopy for food quality analysis

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A.C. Power*, J. Chapman†, S. Chandra*, D. Cozzolino† *Agri-Chemistry Group, School of Medical and Applied Sciences, Central Queensland University (CQU), North Rockhampton, QLD, Australia, †School of Science, RMIT University, Melbourne, VIC, Australia

6.1

Introduction

Spectroscopic analysis deals with the interaction of electromagnetic waves and organic molecules. The use of sensors based on molecular spectroscopy is well recognized within the analytical community because they allow the real-time and simultaneous monitoring of multiple chemical variables or compounds during routine and process analysis, in particular, food samples [1]. Both atomic and molecular spectroscopy are the predominant spectroscopic techniques used in food analysis. These techniques are based on the interaction between light and matter, which result in either the absorption, emission, or scattering of incident electromagnetic radiation [2]. These interactions can be detected by a variety of spectroscopic methods, including ultraviolet-visible (UV-Vis), near-infrared (NIR), mid-infrared (MIR), far infrared (FIR), and Raman spectroscopy, using terahertz waves, microwaves, radio waves, and nuclear magnetic resonance (NMR) over different wavelength ranges of the electromagnetic spectrum [2–6]. As stated, methods and techniques based on molecular spectroscopy are very popular in several stages of food production, including routine analysis, quality control, and bioprocess monitoring. These techniques offer multiple advantages over traditional methods such as the ability to monitor several variables or compounds simultaneously, minimal or no preprocessing of the sample before analysis, low cost, no reagents, and the potential use of fiber optics allowing remote control of the process, among others [7–10]. Fig. 6.1 shows the relationship between spectroscopy and chemometrics for the analysis of food quality. This chapter reviews the basic concepts of UV-Vis spectroscopy and provides examples of this technique applied to the analysis of different food matrices (e.g., meat, milk, coffee, wine, and olive oil).

6.2

The basic principles

6.2.1 Origin of UV-Vis spectra UV-Vis spectroscopy is a method that can monitor and measure the interactions of UV and visible light with different chemical compounds in the wavelength range between 200 and 780 nm. The technique exploits different physical responses of light and Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00006-8 © 2019 Elsevier Inc. All rights reserved.

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Fig. 6.1 The relationship between spectroscopy and chemometrics for the analysis of food quality.

analytes within the sample such as absorption, scattering, diffraction, refraction, and reflection [1]. The phenomenon of UV and visible light absorption is restricted to specific chromophores and several chemical species with defined molecular functional groups [1]. Consequently, the characteristic absorption spectra may be obtained for single molecules because electrons within these chromophores are excited [1]. Quantitative analysis based on UV-Vis spectroscopy is ultimately described by the BeerLambert law and is the correlation between the quantity of the incident light absorbed by the molecule, the sample, the light path length, and the concentration of the absorbing compound or molecule in the matrix [1]. The method allows for the determination and quantification of target molecule concentration within the food matrix [1, 11, 12]. With the continuous development of instrumentation comes improved analytical capabilities, for example, modern fiber optic UV-Vis spectrophotometers with linear photodiode arrays or charge-coupled devices, because detectors are portable due to their compact geometry, highly sensitive, capable of low analyte concentration detection in complex matrices (including aqueous solutions), and efficient (instantaneously reporting a sample’s full spectra) [1, 11, 12].

6.2.2 Sample presentation Solid, liquid, and gaseous samples can be analyzed using UV-Vis spectroscopy. However, the nature of the sample can present challenges for the analyst, because often the sample will be unsuitable for real-world quantitative spectroscopic analysis. Analytical measurements may be hindered by different factors such as the complex nature of the sample, because inaccurate measurements due to interferences or masking agents

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may occur and because of the inability of the analyst to analyze the material as a whole due to the sample’s dimensions; some of these issues can often present in food samples [13–15]. The vast majority of modern commercially available UV-Vis spectrometers are capable of quantitative analysis of chemical and biological samples designed for liquid samples. Solid samples often require a significant level of sample preparation, such as dissolution in an appropriate solvent. Often solid samples are not easily dissolved; if this is the case a more aggressive approach may be necessary, such as acid digestion [13–15]. Analysis of UV absorption solutes can only be performed in homogeneous solutions. In nonhomogeneous samples, particularly where solid particles are present, significant interference is observed within spectra due to the absorption and light scattering effects of the individual particles. In the biotechnology and food industries, this phenomenon is exploited in optical density (OD) measurements that can then be used to determine the biomass concentration in turbid samples. It should be noted that for proteins and other similarly large molecules, incident light is absorbed by multiple functional groups within the compound, which results in nonspecific UV-Vis spectra. Consequently, although the quantification of the total protein concentration of a sample can and often is performed using the Beer-Lambert law via UV absorption at defined single wavelengths, the differentiation of proteins via UV spectra is seldom possible. Many optical density sensors have been developed for the food industry that allow for in-line applications; these sensors are based on transmission or turbidimetry measurements [16–18]. Here the transmission reading of the sample is determined by light absorption over a constant path length and the turbidity is calculated by measuring the light scatter at 90 or 180 degrees [16–18]. However, it must be acknowledged that the OD method is generally considered limited because OD measurements only correlate linearly with cell mass at low concentrations [16–18]. This limitation is further compounded because the technique often only recognizes the presence of particles and cannot distinguish between viable and dead cells, or between intact cells and cell debris or other solid particles [16–18]. Fig. 6.2 shows the effect of path length on the UV-Vis spectra of a series of wine samples. The basic principles of UV-Vis instrumentation are described in work by other authors [19–21]. Recent reviews also highlighted different uses of UV-Vis spectroscopy in bioprocessing and other specific food applications [22–27]. The analysis of solid samples (e.g., meat and flesh foods) by UV-Vis spectroscopy is shown in Fig. 6.3.

6.2.3 Data analysis Food analysis, particularly if using food “stuffs,” is highly complex, variable, and offers significant analytical challenges. Moreover, foodstuffs exist in numerous physical states, such as solids, solutions, emulsions, foams, as well as complicated, heterogeneous systems [28–33]. The many components of food systems, while mainly composed of water, also include carbohydrates, proteins, fats, and other trace constituents such as vitamins, minerals, etc. all contributing to the absorbance spectrum

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Fig. 6.2 Effect of path length on the ultraviolet and visible spectra of alcoholic beverages.

0.0015

Second derivative

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0.0005

0

–0.0005

–0.001

–0.0015

–0.002

UV

Vis Wavelengths (nm)

Fig. 6.3 Second derivative of the ultraviolet and visible spectra of fresh muscles scanned in reflectance.

obtained [28–33]. Food heterogeneity results in considerable spectral complexity, particularly because of the major components (water, carbohydrates, proteins, fats) that dominate the spectra; consequently, conventional approaches regarding the use of spectra are not appropriate and should not be applied. Historically, much of the research in food analysis has been conducted and described as “univariate” in nature, because only the response of a single variable within the overall matrix is examined [28–33]. Because the nature of technology

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has changed, today, relatively speaking, samples are expensive and measurements are cheap. Thus, while analyzing the effect of one variable at a time by analysis of variance techniques does provide useful descriptive information, it is cost prohibitive. Moreover, such analysis is not capable of providing specific information about relationships between multiple variables and other components within the matrix as a whole [34–39]. Chemometrics, multivariate analysis, was developed in the late 1960s, and was introduced by a number of analytical, physical, and organic chemistry-focused research groups. The researchers highlighted that the advancement of instrumentation coupled with the greater availability of computers allowed the measurement of multivariate responses for each sample analyzed [36, 40–43]. Computers and the advancement of modern chemical measurements, where analysts are generally confronted with an abundance of data points, have led to critical information not being readily observable [36, 40–43]. This is particularly evident with spectral data with many different observations (peaks or wavelengths) being collected and where each individual response could be considered resulting from a different dimension of the overall sample. Traditionally, analysts have endeavored to simplify and consolidate analysis measurement by isolating or extracting the analyte of interest to ultimately eliminate potential matrix interference in methods [36, 40–43]. However, using methods to isolate or extract the analyte of interest fails to account for the potential chemical and physical interactions between total components of the sample—this is especially evident for complex foodstuffs such as grapes and wine. Because univariate models do not consider the contributions of multiple variable sources the models on which the analysis is based can be oversimplified and as a result be limited. Therefore it is necessary that the sample in its entirety is analyzed and not just at a single component to ultimately untangle all the complicated interactions between the constituents and understand their combined effects on the whole matrix. Multivariate methods provide the means to move beyond the one-dimensional (univariate) approach. In the majority of many cases reported, multivariate analysis highlights the constituents that are genuinely important/influential by investigating the various interferences and interactions within the sample that univariate analysis ignores [12, 36–39, 41–44]. Today, food quality measurement techniques tend to be multivariate and are based on indirect measurements of the chemical and physical properties of the sample [35, 44–46]. A common characteristic of the most useful of the instrumental techniques utilized is that paradoxically the measurement variable rarely possesses a direct relationship with the property of interest, for example, the particular concentration of an analyte in the sample—so the majority of techniques are correlative methods [35, 44–46]. This is best explained when the chemical and physical interferences of the analysis are considered. Spectroscopic techniques provide an incredible amount of information from a single measurement, because they have the capability to record the response at multiple wavelengths simultaneously [35, 44–46]. As a result, it is necessary to use multivariate analysis to properly extract the information of interest from the total data. There is a growing portion of the literature where further information on the numerous algorithms, formulas, and procedures exploited by multivariate analysis can be sourced [34–39, 41–46].

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Table 6.1 Advantages and limitations of ultraviolet and visible spectroscopy to analyze foods Advantages Sensitivity of the application Cost of instruments Remote sampling Sample average Solid samples Slurry samples

High sensitivity Relatively low cost Available using fiber optics Good to very good depending on the optics Analyzed using reflectance Analyzed using transflectance or fiber optics

High chemical resolution Effect of path length Qualitative analysis (multiple wavelengths) Quantitative analysis

6.3

Limitations

Linear regression

Effect of scattering and path length No Sensitive to changes in path length Need for chemometric tools (principal components) Multivariable calibration needs of chemometric tools

Advantages and limitations of UV-Vis spectroscopy

Table 6.1 summarizes some of the advantages and limitations of the use of UV and VIS spectroscopy to analyze foods. These advantages and limitations include sensitivity of the application, cost of instruments, potential use on remote sampling, analysis of liquid, solid and slurry samples, chemical resolution, and the effect of path length, among others.

6.4

Recent applications and progress of UV-Vis spectroscopy in different types of foods

The usefulness of UV-Vis spectroscopy (often in combination with other spectroscopic or chromatic techniques) coupled with chemometrics for food analysis is evident by the range of foodstuffs that have been analyzed by the method reported in the literature. The following are some examples of applications of UV-Vis spectroscopy in different food materials and commodities.

6.4.1 Coffee Dankowska and colleagues [47] reported the use of UV-Vis spectroscopy in combination with fluorescence to quantify the concentrations of roasted Coffea arabica and

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Coffea canephora var. robusta in 33 different coffee blends from 15 different countries. The authors applied chemometric techniques such as principal component analysis (PCA) to reduce data multidimensionality [47]. Linear discriminant analysis (LDA) was also used to determine the percentage of bean type in each blend. The classification rate obtained based on the UV-Vis spectra was over 96% [47]. The results reported by these authors determined that such analysis would contribute significantly to the reduction of food fraud and better protect the interest of consumers [47]. In a similar study, Souto and collaborators [48, 49] conducted studies where UV-Vis spectroscopy combined with chemometrics was used to analyze coffee. In their 2010 work [48], the authors compared two methodologies, successive projections algorithm (SPA)-LDA and soft independent modeling of class analogy (SIMCA), as a means to classify between caffeinated and decaffeinated roast coffee [48]. The SPA-LDA model demonstrated a greater ability to discriminate the conservation states of the 43 samples than SIMCA and more promisingly retained a high classification accuracy (96%) despite the introduction of artificial noise into the spectra [48]. The authors further investigated the potential of UV-Vis paired with chemometrics in a work reported in 2015 [49], where they demonstrated the technique’s ability to identify the adulteration of roasted ground coffee with husks and other organic matter. These authors stated that SPA associated with LDA is the most appropriate method for classification and that the group’s proposed protocol is advantageous because it is simple and rapid with very little sample preparation—extraction with hot water alone [48].

6.4.2 Milk A number of studies in the literature detail the use of UV-Vis spectroscopy to monitor melamine content in milk products using gold nanoparticles as probes [50–54]. The method is generally based on the aggregation of gold nanoparticles in the presence of melamine resulting in a color change of the nanoparticle solution from wine red to purple [50–54]. The authors of these studies highlight the practicality, speed, simplicity, and reliability of the technique, in comparison to other published assay approaches that require high cost equipment and complicated pretreatments [50–54].

6.4.3 Olive oil UV-Vis spectroscopy is widely used due to its great versatility, easy handling, high sample turnover, and automation feasibility. However, the low selectivity of this technique makes its application, without a previous pretreatment, an almost impossible task. In fact, its direct application on VOO and EVOO analysis is limited to a few cases (e.g., anisidine value, peroxide value, general color, carotenoids, and chlorophylls). Nevertheless, chemometrics has proven to be very useful to deal with the major issues of this methodology. A similar scenario appears in the application of fluorimetric techniques, which find selectivity and improve their performance in combination with chemometric techniques. A good example of this tendency is the work of Torrecilla and collaborators [55], who utilized UV-Vis spectroscopy to quantify the adulteration

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of extra virgin olive oil with refined olive oil and olive-pomace oil [55]. This work reported that the technique was capable of estimating the adulteration agent concentration with a mean square error of less than 1%, with the authors determining that the method is appropriate not only for adulteration detection but also for the measurement of impurities within higher grade olive oils [55]. Mignani and collaborators combined UV-Vis spectroscopy with chemometrics to produce multiple specific defect models for extra virgin olive oil [56]. To generate the models, the UV-Vis spectrum was preprocessed using first derivatives and second-order smoothing polynomials through seven points [56]. Prior to derivatization, standard normal variate, offset, and baseline (third-order) corrections were applied before the resulting spectrum was mean centered [56]. The spectral range of 580–1000 nm provided the best results to classify the specific defects and the whole spectrum (300–1000 nm) was suitable for general nondefective and nonedible oils. The first region corresponded to the Vis range, which showed the presence of dyes and pigments [56]. A range from 380 to 450 nm belonged to carotenoid pigments that had high stability. However, chlorophylls and pheophytins, with an exclusive absorption band at 650–700 nm, had a high influence on the model for all of the defects. According to the authors, this may be attributed to the defective samples that have undergone a degradation process for different reasons, which may have affected the composition of these substances [57, 58]. Another band near 935 nm had significant influence on the classification model for winey and rancid samples. In general, peaks at 610 and 670 nm are reduced for all of the degenerated or defective olive oils. The adulteration of olive oil with inferior substitutes using lowfield (LF) proton (1H) NMR relaxometry and UV-Vis spectroscopy was conducted by Ok [59]. In his investigation, three different olive oils with different oleoyl acyl contents were mixed with almond, castor, corn, and sesame oils. The author determined that both LF 1H NMR relaxometry and UV-Vis spectroscopy were required to quantitatively detect the adulteration concentration. In another study by Casale and collaborators [60], olive oil samples were analyzed using a combination of spectroscopic (UV-Vis, NIR, and MIR) and chemometric techniques [60]. The authors found that the spectra and composition of the olive oil were influenced by multiple factors, including agricultural and harvesting methods, transport and storage conditions, and climate. The work also determined that the characterization of Chianti Classico PDO olive oils was best achieved by combining NIR and UV-Vis analysis [60].

6.4.4 Tea Diniz and colleagues reported the use of chemometrics and UV-Vis to simultaneously classify both the geographic origin and variety of teas using water extraction [61]. The authors reported that SPA-LDA and PCA-LDA was significantly better for tea classification of the five studied classes (Argentinean green tea, Brazilian green tea, Argentinean black tea, Brazilian black tea, and Sri Lankan black tea) with the reported methodology being superior to traditional tea quality evaluation methods because it is a simpler, faster, and more affordable classification [61]. In another study, Wang and collaborators [62] combined chemometrics, UV-Vis, and NIR spectroscopy to classify five different green tea varieties. These authors reported that the developed method affords a useful low-cost means for the rapid classification of green teas [62].

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Similarly, Pauli and collaborators combined chemometrics and UV-Vis spectroscopy to discriminate between Camellia sinensis tea leaves [63]. The authors highlighted the advantages of the proposed technique in terms of simplicity, data acquisition, time, and cost [63].

6.4.5 Vinegar Torrecilla and colleagues combined UV-Vis spectroscopy and chemometric modeling to identify vinegar blends via their raw materials (red or white wine, cider, apples, rice, and molasses) [64, 65]. In their work, the authors utilized both partial least squares discriminant analysis and artificial neural networks [64, 65]. The average correct classification rate of a series of comparable internal validations was around 55% and 90% for the PLS-DA and ANN models, respectively [64, 65], with the authors reporting the design of an accurate chemometric tool for the detection of specific vinegars in mixtures in an inexpensive and straightforward manner [64, 65].

6.4.6 Wine The use of UV-Vis spectroscopy by the wine industry is not new. This technique has been used routinely by the industry to measure phenolic compounds and color. UV-Vis spectroscopy has been applied to the classification of white wines because the relevant wavelengths fall in the spectral range of 240–400 nm, which relates to esters and hydroxycinnamic acids, and it is highlighted that the technique requires coupling with pattern recognition methods [66]. However, the anthrocyanin and other phenolic concentrations of red wines are reflected over the 250–800 nm range [67–71]. The technique was evaluated to discriminate between wines from different geographical regions in terms of monitoring quality [72–74] as well as a potential method to detect wine adulteration [75].

6.4.7 Meat and fish The potential use of UV-Vis, NIR, and MIR spectroscopy, combined with chemometric techniques, to measure the adulteration of minced beef with turkey meat was evaluated [76]. The spectral data was processed and then analyzed using PCA, LDA, and PLS regression. According to the authors of this study, the best results were obtained with NIR and MIR spectroscopy, whereas the UV-Vis results were less satisfactory [76]. However, a combination of information from UV-Vis with NIR and MIR spectroscopy improved the overall results [76]. UV-Vis spectroscopy was used to classify Japanese dace fish into fresh or spoiled samples using support vector machine (SVM), LDA, PCA, and SIMCA as classification techniques [77]. The authors reported that classification models based on UV-Vis spectra (250–600 nm) and SVM correctly classified 100% of the fresh fish samples analyzed. Similar results were reported by the same authors comparing different classification methods [78, 79]. The so-called artificial fish swarm algorithm (AF) for the synchronous selection of wavelengths and different pretreatment methods was evaluated to quantify the level of

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beef adulteration with spoiled beef [80]. The authors of this study evaluated different vibrational spectroscopy techniques such as UV-Vis, NIR, and UV-Vis-NIR [80]. The best classification model was based on the combination of Vis-NIR [80]. The authors demonstrated that AF was a useful tool for model optimization as compared with other techniques such as a genetic algorithm [80].

6.4.8 Saffron A stepwise approach was used as a screening tool to identify and monitor the origin of adulteration of saffron during trade with carminic acid [81]. This natural dye is of insect origin and should not be present in Kosher and Halal foods such as saffron [81]. The authors reported the use of UV-Vis spectroscopy to detect gross adulteration levels (>25.0%, w/w) based on the reaction with diphenylamine-sulfuric acid [81]. According to the authors, UV-Vis spectroscopy was able to detect adulteration down to the level of 2.0% (w/w) [81].

6.4.9 Propolis Propolis is a beneficial natural product and has been used in the food and pharmaceutical industries as a food preservative [82]. In this study, UV-Vis spectroscopy and cyclic voltammetry were combined with PCA to confirm the presence of two botanical subtypes of propolis [82]. The results reported by these authors confirmed that UV-Vis spectroscopy combined with chemometrics has the potential to discriminate complex natural products such as propolis [82].

6.5

Summary and outlook

In the literature, there are numerous reports of the use of UV-Vis spectroscopy (both individually and combined with other spectroscopic methods) in food analysis (qualitative and quantitative analysis). Technique popularity is due to the common availability of instruments, simplicity of use, speed, precision, accuracy of the analysis, and the relatively low cost. In recent years, UV-Vis spectroscopy techniques have also been evaluated as useful techniques for monitoring several compounds simultaneously during different processes (e.g., in line, at line). However, the development of such applications requires the use of multivariate data analysis methods or chemometrics. Such methods based on UV-Vis spectroscopy will offer the possibility to provide noninvasive and remote analysis of foods in industrial settings. However, various barriers still hinder the growth and development of these applications by the food industry. Among them, the hesitancy of the food industry to accept the integration of chemistry and mathematics (the benefits of chemometrics are often ignored by those who prefer to employ classical statistics) and the lack of academic education and skills in the use and application of instrumental methods based in UV-Vis spectroscopy as high-throughput tools for process monitoring.

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