Developments of nondestructive techniques for evaluating quality attributes of cheeses: A review

Trends in Food Science & Technology 88 (2019) 527–542

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Review

Developments of nondestructive techniques for evaluating quality attributes of cheeses: A review

T

Tong Lei, Da-Wen Sun∗ Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland

ARTICLE INFO

ABSTRACT

Keywords: Cheese Quality evaluation Spectroscopic techniques Imaging techniques Nondestructive techniques

Background: Cheese is produced around the world in a wide range of flavours, varieties, textures, and shapes, which can be used as a final product in human diet, and as an important ingredient in various foods. With consumer's continuing demand for quality cheese as well as increasing challenge from production facing the industry, nondestructive techniques are increasingly used to evaluate cheese quality. Scope and approach: Considering the rapid development of novel nondestructive techniques, relevant literatures in the past 15 years (2004–2018) are reviewed in this paper. The main quality attributes of cheese and the importance of evaluating these attributes are discussed. The principles, developments and applications of computer vision (CV), computed tomography (CT), X-ray system, magnetic resonance imaging (MRI), fluorescence spectroscopy, near-infrared (NIR) spectroscopy, Fourier transform infrared (FTIR) spectroscopy, hyperspectral imaging (HSI), Raman imaging, ultrasonic and acoustic sensing and other nondestructive techniques are discussed in this review. Key findings and conclusions: Among all nondestructive techniques used for cheese quality evaluation, fluorescence spectroscopy is the most used method for classification, NIR spectroscopy is mainly used for predicting chemical components, FTIR spectroscopy shows the greatest scope of applications, while CV, X-ray and MRI are only used for monitoring eye growth. HSI and Raman imaging begins to attract research interest, and synchronous fluorescence spectroscopy gradually replaces front-face fluorescence spectroscopy, whereas CV is no longer applied to cheese evaluation and literatures about NIR spectroscopy also becomes less in recent years. It is hoped that this review should provide information on current research gaps and set some directions for future studies.

1. Introduction Dairy products are very popular around the world and have high commercial value in the food industry. Substantial increases in global milk production over the last decade have resulted in the manufacturing of significantly increased production of cheese, butter and milk powders (Dankowska, Małecka, & Kowalewski, 2015; Panikuttira, O'Shea, Tobin, Tiwari, & O'Donnell, 2018). Cheese, as one of the oldest and most important dairy food products, is produced worldwide with various flavors, types, and appearances, and is regarded as a crucial dietary source of protein, fat and other salts (Pi et al., 2009; Vásquez et al., 2018; Jeliński, Du, Sun, & Fornal, 2007). Cheese is consumed as a final food product in human diet or as an important ingredient in various foods (Ozbekova & Kulmyrzaev, 2017), and evaluation of

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quality attributes of cheese is therefore important for consumers and the industry. In recent decades, novel rapid and nondestructive methods for evaluating cheese quality attributes are needed and developed (Abbas et al., 2012; Barreto et al., 2018; Downey et al., 2005). Unlike conventional methods, these novel methods acquire data without contact with samples, thus providing nondestructive measurements (Wu & Sun, 2013), and computer vision, near-infrared spectroscopy, Fouriertransformed infrared spectroscopy, Raman spectroscopy, hyperspectral imaging and other techniques have been developed for cheese production process monitoring (Currò et al., 2017; Everard et al., 2007; Ferragina et al., 2013; Loudiyi et al., 2018b; Vásquez et al., 2018), and determination of quality, geographical origin and adulteration of cheese products (Barreto et al., 2018; Cevoli et al., 2013; Subramanian

Corresponding author. E-mail address: [email protected] (D.-W. Sun). URLs: https://www.ucd.ie/refrig, https://www.ucd.ie/sun (D.-W. Sun).

https://doi.org/10.1016/j.tifs.2019.04.013 Received 2 December 2018; Received in revised form 5 March 2019; Accepted 18 April 2019 Available online 19 April 2019 0924-2244/ © 2019 Elsevier Ltd. All rights reserved.

Trends in Food Science & Technology 88 (2019) 527–542

T. Lei and D.-W. Sun

et al., 2009b). Several reviews on nondestructive techniques for food quality evaluation have been published, including evaluation of fish and fish products (Xu et al., 2015), powdery foods (Su & Sun, 2018), and mango (Jha et al., 2010). However, no review is available focusing on the applications of nondestructive techniques for cheese quality evaluation in the last few years. The only relevant review available is on cheese process control (Panikuttira et al., 2018). Due to the extensive studies in this area, the current review intends to provide an overview on the applications of novel nondestructive methods including computer vision, spectroscopies, spectral imaging, ultrasound, and other techniques for providing rapid evaluation of quality attributes of cheese and cheese products. In this review, the importance of evaluating these attributes is presented, principles of these techniques are highlighted, different application scenarios and the pros and cons of employing these techniques are discussed, and research trends of these techniques for the cheese industry are also proposed. It is hoped that the review can provide advice and guidance to both scientific research and industrial application in the area of cheese quality evaluation, point out current research gap, and become a signpost for future study.

et al., 2015). Fat contents can affect texture, rheology and sensory attributes of cheese (Fox et al., 2004). Cheese has a high concentration of protein, which is almost fully digestible. Normally, the protein content of cheese varies inversely with the fat content (Fox et al., 2004). Moisture has the highest content in cheese, hence controlling moisture during cheese processing has technical connotation for final cheese quality (Everard et al., 2007; Fox et al., 2004). Other chemical components in cheese include fatty acids, amino acids and salt. Cheese is an important dietary source of several minerals, especially calcium, sodium, phosphorus and magnesium (Fox et al., 2004). Besides, the use of salt (NaCl) in cheese to minimize spoilage and limit the growth of pathogens also affects flavors of cheese. NaCl in cheese can be substituted by KCl, MgCl2, and CaCl2 in order to reduce Na intake (Fox et al., 2004; Loudiyi & Aït-Kaddour, 2018). Most of fat-soluble vitamins in milk, especially vitamin B12, are retained in the cheese fat (Fox et al., 2004). Some other compositions, such as starches and flours, may be added into fresh cheese in doses for functional purpose. The maximum concentration of additives like starch, stabilizer or anti-caking agent in cheese product is 5 g kg−1 (Barreto et al., 2018). The physical attributes of cheese include rheology, texture, sensory and some other specific attributes. Texture can be defined as an attribute of cheese originating from a group of physical properties, such as hardness, cohesiveness, elasticity, viscosity and adhesiveness (Fox et al., 2004). Cheese color is also an important physical parameter, which can be considered as an indicator for many attributes like flavour, sanity and maturity (Dufossé et al., 2005). In process control during cheese production, monitoring process parameters and maturity are the key. Maturity of cheese is a complicated process that involves several concurrent and interlinked reaction including pH changes, lipolysis and proteolysis (Calzada et al., 2014a; Karoui et al., 2006c; Kulmyrzaev et al., 2005; Soodam et al., 2014; Subramanian et al., 2011). Chemical and physical changes occurring during ripening cause the body of the fresh cheese to lose its hard texture and to become soft, which depends upon the degree of proteolysis and the duration and temperature of maturity (Fox et al., 2004; Kulmyrzaev et al., 2005). Important attributes during maturity include

2. Quality attributes of cheese 2.1. Main quality parameters Evaluating the cheese quality involves controlling its flavour, texture, chemical composition and appearance (Smith et al., 2017). In this review, cheese quality attributes are divided into different categories shown in Fig. 1. For products produced during manufacturing like curd, fresh cheese and final cheese product, their important attributes include chemical components, internal structure, physical properties and other attributes such as oxygen and dielectric properties. In chemical attributes, fat, protein and moisture are the three major compositions in cheese. In bovine dairy products, fat originates from raw bovine milk, which contains about 3.5% milk fat (Zhao, Beattie, Fearon, O'Donnell, & Downey, 2015), and as cheese is produced from milk, this means that the only fat in cheese is milk fat (Dankowska

Fig. 1. Main quality attributes of cheese. 528

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cheese age, which is the length of ripening period (Subramanian et al., 2011), and the ratio of water-soluble nitrogen (WSN) to total nitrogen (TN) (Currò et al., 2017). Besides, the process of ripeness has other signs like eye formation in cheese matrix (Darnay et al., 2017). The subtle balance between the increase of volume of CO2 gas within the eye and the mechanical resistance that the surrounding cheese presents to CO2 gas can significantly affect eye formation during ripening (Darnay et al., 2017; Grenier et al., 2016). Attributes during syneresis and salting stages are defined as processing parameters in this review (Fig. 1). The curd shrinks because of the realignment of casein micelles, which leads to the exclusion of whey from the curd matrix, therefore, most of the moisture and lactose are removed during this process (Everard et al., 2007). Monitoring moisture change during this stage thus becomes very important. Cheese or curd yield, representing the quantity of cheese or curd obtained from a given quantity of original milk, is one of the most crucial economic parameters for the cheese industry (Ferragina et al., 2013). Nutrition recovery, which is defined as the ratio of the contents of fat, protein, total solids, and energy in curd over the content of the corresponding nutrient in original milk, is another important attribute that reflects the losses of milk individual nutrients in whey (Ferragina et al., 2013). For cheese final products, major attributes for quality evaluation are sensory, heat-induced properties and classification of cheese. Sensory attributes result from interactions of the human sensory forms of vision touch, gustation, olfaction and mouthfeel and are stimulated by physical properties, internal structure and chemical components of the cheese (Fox et al., 2004). Evaluating the heat-induced physical properties such as meltability, oiling off and browning of melted cheese also attracts much attention. Meltability is dependent on the thermal phase change of solid cheese and the rheology of melted cheese (Amamcharla & Metzger, 2015). Oiling off of cheese is expected to be obvious during cooking, which means when increasing cooking temperature, more free oil is separated out from cheese matrix (Wang & Sun, 2004b). The melting temperature of cheese is also a crucial heat-induced attribute that characterizes the readiness for implementation and transition of the product from one phase to another (Ozbekova & Kulmyrzaev, 2017). Nondestructive techniques are also suitable for classifying cheese products based on different attributes. One of such important attributes is protected designation of origin (PDO), which represents a system of distinction for a specific quality and a certification of the uniqueness of cheese products for consumers and is also an effective marketing method for the producers (Kulmyrzaev et al., 2008; Oca et al., 2012).

2011). Nowadays, food related legislation is set on the basis of fair trade, food security and consumer right (Barreto et al., 2018). At the same time, more and more new cheese products appear in the market. For example, new types of cheese like process cheese with vegetable ingredients that can be added to sandwiches, salads, pizza and other products (Jeliński et al., 2007). The need to establish standards of identities for cheese products is essential for a better control of product quality because legislations to some new types of cheese are still blank in some countries (Madalozzo et al., 2015). Detection of adulteration is also important in food quality evaluation. Adulteration is an intentional act that reduces the food quality for economic purposes, either by mixing, substituting or removing some food compositions with high value (Dankowska et al., 2015; Barreto, Cruz-Tirado, Siche, & Quevedo, 2018). For instance, milk fat is one of the most expensive commercial fats, therefore, cheese-like products are normally produced by partially or totally substituting milk fat by much cheaper plant oils (Dankowska et al., 2015). Therefore, detection of foreign fat in cheese products is desired to avoid adulteration (Dankowska et al., 2015). In addition, classification between various types of cheese also requires quality evaluation (Kim et al., 2014). For example, lysinoalanine, which exists in commercial milk, can be used to identify natural and imitation Mozzarella cheeses (Kim et al., 2014). Traditionally, chemical components of cheeses are measured by some physicochemical methods, such as Kjeldahl method or Dumas combustion method (for nitrogen), Mojonnier method (for fat), and vacuum oven method (for moisture) (Amamcharla & Metzger, 2015; Baghdadi et al., 2018; Henriques et al., 2013; Ray et al., 2016; Woodcock et al., 2008). For the analysis of amino acids and peptides, the most common technique is high performance liquid chromatography (HPLC), while gas chromatography (GC) can also be used to analyse cheese amino acids (Subramanian et al., 2011), and fatty acid and phytosterol contents (Calzada et al., 2014b; Chen et al., 2012; Kim et al., 2014). Conventional methods used for cheese rheology determination are dynamic rheological measurements (small angle oscillation) and tube viscometry techniques (Ozbekova & Kulmyrzaev, 2017; Ray et al., 2016). Texture profile analysis (TPA) is a widely used instrumental technique to evaluate textural parameters (Amamcharla & Metzger, 2015; Dagostin et al., 2013; Oliveira et al., 2011; Vásquez et al., 2018). Dynamic stress rheometry (DSR), Schreiber test, Olson and Price test and rapid visco analyser are several popular methods to measure the meltability (Amamcharla & Metzger, 2015; Fagan et al., 2007a; Giri et al., 2014; Ray, Gholamhosseinpour, Ipsen, & Hougaard, 2016). In addition, present methods for cheese internal structure (defect) detection and sensory evaluation are by human expert inspection: the cheese samples are tapped by hand or a small hammer, and the samples are discriminated based on the emitted sound (Conde et al., 2008), while sensory evaluation is always performed by trained panels (Jeliński et al., 2007; Everard et al., 2005; Kraggerud, Næs, & Abrahamsen, 2014; Picon et al., 2013; Subramanian, Harper, & Rodriguez-Saona, 2009a, 2009b). Nevertheless, most of the methods mentioned above are destructive, time-consuming, labour-intensive, expensive and are not chemical-free (Woodcock, Fagan, O’Donnell, & Downey, 2008; Subramanian, Alvarez, Harper, & Rodriguez-Saona, 2011; Ozbekova & Kulmyrzaev, 2017; Pi, Shinzawa, Ozaki, & Han, 2009). For instance, cheese samples need to be dried at high temperature in order to measure the moisture content (Ray et al., 2016); GC analysis requires extraction of fat from cheese, thus destroying the sample, requiring significant resources and increasing the testing time (Zhao et al., 2015); eye growth in cheese can only be measured once using conventional methods during ripening, and the cheese sample is cut into halves for measuring volume and number of eyes, thus the growth of these eyes could not be further monitored (Grenier et al., 2016). On the other hand, the cheese

2.2. Importance of nondestructive techniques in cheese quality evaluation Understanding the structure of cheese, especially structures of protein and fat, and the mutual effects between different cheese components during and after manufacture and after cooking, can provide useful information on determining what defines a product with good quality and make it possible to predict quality attributes before entering the market (Kraggerud et al., 2014; Mazerolles et al., 2001; Vásquez et al., 2018). For example, when the starch content in fresh cheese is exceeded, the nutritional quality could be affected, whereas it is difficult to distinguish the excessive starch for consumers (Barreto et al., 2018). Quality attributes of cheese such as melting, rheology, texture, color sensory and internal structure can also influence consumer's acceptability and preference (Amamcharla & Metzger, 2015; Conde et al., 2008; Panikuttira et al., 2018; Wadhwani & McMahon, 2012). Previous researches were also interested in investigating the interacting effects between different quality attributes of cheese: the increase of age resulted in increasing hardness and reduced activation energy for initiation of fat melting (Ray et al., 2016), low-fat content in cheese might reduce its melting (Ozbekova & Kulmyrzaev, 2017), the pH of cheese might be affected by the content of salts (Fox et al., 2004), and ripening time can influence the sensory attributes of cheese (Subramanian et al., 529

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production and manufacturing techniques are currently at the stage of dynamical innovation (Jeliński et al., 2007). Therefore, a large amount of analyses during the production and storage of cheeses products are highly demanded (Abbas et al., 2012; Karoui et al., 2006a). However, in the cheese industry worldwide, some cheese manufacturing processes such as syneresis and ripening still rely on empirical control (Everard et al., 2007). Thus, evaluation of processing parameters including ripening period, cheese or curd yield, nutrition recovery in large-scale cheese factories becomes a challenged aspect in the cheese production management (Margolies & Barbano, 2018; Subramanian et al., 2011). Even though such conventional measurement techniques are practicable for measuring cheese quality attributes and quality control in cheese manufacture (Kim et al., 2014; Ray et al., 2016), rapid, costeffective and nondestructive methods are still needed for quality evaluation of cheese products in both academic and industrial settings (Amamcharla & Metzger, 2015; Woodcock et al., 2008), and much progress has therefore been made in developing nondestructive methods such as imaging and spectroscopic techniques for evaluating cheese quality attributes in recent years (Darnay et al., 2017; Kraggerud et al., 2014).

vision technique was combined with conventional methods such as Schreiber test to increase the accuracy of results (Everard et al., 2005; Wang & Sun, 2004a). One limitation of computer vision is that optical measurements is only conducted on the sample surface, which may confuse the prediction (Everard et al., 2007), and result in the difficulty in evaluating attributes of a block of cheese (Jeliński et al., 2007). In addition, computer vision cannot acquire chemical information from samples, which limits the use of this technique in the industry. More advanced image acquisition techniques, which can capture the images with internal structure, such as computed tomography (CT), X-ray system and magnetic resonance imaging (MRI) are thus developed for cheese quality evaluation. 3.2. Computed tomography Advanced nondestructive imaging techniques, such as computed tomography (CT), X-ray system, and magnetic resonance imaging (MRI), can provide three-dimensional (3-D) images (Fig. 2), (Musse et al., 2014). There are several advantages of CT, including fast testing speed and extensive availability of the scanners (Lee et al., 2012). CT is a very accurate detection method without destructing any mechanism in cheese. Obtained images reliably represent the 3-D internal structure in cheese samples (Schuetz et al., 2013). Guggisberg et al. (2013) indicated that CT might be considered as a powerful tool to monitor the eye inflation in cheese with very high R2 of > 0.998. Schuetz, Guggisberg, Fröhlich-Wyder, and Wechsler (2016) revealed that the accuracy of monitoring cheese eye formation using CT changes slightly with different analytical software, but the R values still maintain a very high level between 0.9990 and 0.9999. Thus, CT is normally regarded as a reference method in many literatures about monitoring eye growth during cheese ripening (Guggisberg et al., 2015; Huc et al., 2014a; Lee et al., 2012; Schuetz et al., 2013).

3. Imaging techniques 3.1. Computer vision Computer vision (CV) employing image processing techniques had been developing rapidly in the early 21st century (Table 1). The system comprises of five essentials: light sources, a camera, an image capture board and computer hardware and software (Sun, 2000). Computer vision system was used in the food industry for goals of inspection and evaluation as it allows to maintain accuracy, objectivity and consistency while eliminating the subjectivity of empirical inspections and reducing economic expenses (Jeliński et al., 2007; Wang & Sun, 2004a; Du and Sun, 2005; Jackman et al., 2011; Sun and Brosnan, 2003; Zheng et al. 2006). There were many researches on using computer vision for inspecting quality attributes of cheese in the past years, and these researches mainly focused on physical attributes, especially heat-induced properties (Table 1). Another attractive research area for this technique is inspecting distribution and area (amount) percentage. The percentage and distribution of cheese topping on pizza were evaluated with an average accuracy of 90% (Sun, 2000). The percentage of the total surface area occupied by cheese eye was calculated with the coefficient of variation ranging from 2.43% with eyes of about 1% of the surface of cheese slice to a coefficient of variation of 0.92% with eyes of about 6.8% of the surface area of cheese slice (Caccamo et al., 2004). The amount of ingredients in two types of pasteurized process cheese were predicted with determination coefficients (R2) of 0.7168 and 0.8884, and the grading on process cheeses were successfully carried out with R2 values of 0.8191 and 0.8898 according to the ingredient distribution (Jeliński et al., 2007). In addition, compositions in cheese can also be evaluated by computer vision. During the syneresis phase of cheese manufacture in a stirred-curd context, curd moisture, yield of whey and solids in whey per kilogram of milk were predicted by computer vision with standard error of prediction of 18%, 14%, and 14% with correlation coefficients (R) of 0.78, 0.73, and 0.73, respectively (Everard et al., 2007). In the aspect of evaluating cooking attributes, researchers using computer vision system found that the influences of baking temperature, time and their interaction on browning property of cheddar and mozzarella cheese were significant (P < 0.0001) (Wang & Sun, 2003). In the study of evaluating cheese oiling off property using CV (Fig. 2), Wang and Sun (2004a) showed that percentage oil area and fat leakage had a correlation with R of 0.852, they also revealed that dimensions of cheese could significantly affect its oiling off property, in addition cheese slice dimensions including area and thickness could significantly influence its meltability (Wang & Sun, 2002). Moreover, computer

3.3. X-ray imaging X-ray can penetrate through multiple layers of sample without fully attenuation during interacting with the sample (Kotwaliwale et al., 2014). Therefore, X-ray image is a transmittance projection of the sample coming across the photons path. Current studies on the application of X-ray in the quality evaluation of cheese are limited (Table 1). Similar to CT, X-ray was normally used to monitor eye formation in cheese (Kraggerud et al., 2009). Schuetz et al. (2013) compared X-ray with CT and found that the number of eyes and total eye volumes obtained by X-ray were only 37.8–83.3% and 9.4–51.0% of those determined by CT. An important reason was that the identification of overlapping eyes in cheese was difficult for X-ray, and development of novel algorithms are thus necessary (Kraggerud, Wold, Høy, & Abrahamsen, 2009; Schuetz et al., 2013). 3.4. Magnetic resonance imaging The principle of MRI is that magnetic behaviour of some specific nuclei in a cheese sample placed in an external magnetic field while being subjected to radio frequency pulses. MRI can provide the internal structures and growth kinetics of eyes in cheese during ripening (Huc et al., 2014b). A 3-D MRI method, coupled with a corresponding image processing algorithm allowing for separation of overlapping eyes and labelling individual eye, provided a non-destructive method for monitoring eye formation and development in semi-hard cheese with R2 > 0.997 (Musse et al., 2014). Grenier, Laridon, Le Ray, Challois, and Lucas (2016) also monitored eye volume in semi-hard cheese during ripening using MRI. Due to its high accuracy, MRI was also used as a reference method for investigating eye formation (Huc et al., 2014b). CT, X-ray and MRI were only used to evaluate eyes but their uses for 530

531

CV CV CV

Eye

Meltability Processing parameters

Ingredients

Eye Eye Eye Eye

Eye Eye Eye

Eye Eye Eye Eye

Eye

Process cheese

Gouda-type cheese Tilsit-type cheese (Semi-hard) Emmental cheese (Hard) Cheese (Semi-hard)

Swiss-type cheese Swiss-type cheese Cheese (Semi-hard)

Tilsit-type cheese (Semi-hard) Emmental cheese (Hard) Cheese (Semi-hard) Cheese (Semi-hard)

Cheese (Semi-hard)

Classification of geographic origin Classification of geographic origin Rheology, classification of manufacturing Classification of manufacturing Classification of milk and whey Classification of brand Sensory Rheology

Emmental cheese

Emmental cheese Norvegia cheese (Semi-hard) Tilsit cheese (Semi-hard)

Cheese (Soft)

Cheese (Soft)

Cheese (Soft)

Cheese (Hard)

Classification of geographic origin

Emmental cheese

Spectroscopic Techniques

CV

Oiling off

FF FF FF

FF

FF

FF

FF

FF

FF

MRI

X-ray X-ray MRI MRI

CT CT X-ray

CT CT CT CT

CV

CV

Nondestructive techniques

Oiling off

Attributes

Imaging Techniques Cheddar and Mozzarella cheese Cheddar and Mozzarella cheese Ragusano, Emmental, and Cheddar cheese Process cheese Cheese

Types of Cheese

Normalisation Normalisation Normalisation, SNV

Normalisation

Normalisation

Normalisation

Normalisation

Normalisation

Median filter, 2D polynomial

Pre-processing Methods

Table 1 Applications of nondestructive techniques for cheese quality evaluation between 2004 and 2018.

PCA, PLSR, ANOVA PCA, PLSR

CCSWA

PCA, CCSWA

PCA, Factorial Discriminant Analysis, CCSWA PCA, Factorial Discriminant Analysis PCA, Factorial Discriminant Analysis CCSWA

Self 3D reconstructed algorithm

PCA

ANOVA

KR, HT

ANOVA Linear model

ANOVA

Mathematical Models

(Inertia) Brand classification = 23–78% (R) Sensory = 0.39, Amino N = 0.84, Lactic acid = 0.72 (R2) Fat = 0.98, Moisture = 0.99, Protein = 0.98, Melting temperature = 0.99, Rheology = 0.62–0.90

(Inertia) Manufacturing classification = 88%

Classification success rate = 60%

Classification success rate = 44.20%

Classification success rate = 90.50%

(R2) Eye volume > 0.997

(R) Eye volume = 0.9990–0.9999

(R) Moisture = 0.78, Yield of whey = 0.73, Solids in whey = 0.73 (R2) Amount of ingredients = 0.717–0.888, Grading = 0.819–0.890

Coefficient of variation = 2.43% (1% area of eye), 0.92% (6.8% area of eye)

(R) Fat leakage = 0.852

Results

(continued on next page)

Botosoa and Karoui (2013) Kraggerud et al. (2014) Ozbekova and Kulmyrzaev (2017)

Karoui, Dufour, Schoonheydt, and De Baerdemaeker (2007) Kulmyrzaev and Dufour (2010)

Kulmyrzaev et al. (2005)

Karoui et al. (2005)

Karoui et al. (2004b)

Karoui et al. (2004a)

Guggisberg et al. (2013) Schuetz et al. (2013) Huc et al. (2014b) Musse, Challois, Huc, Quellec, and Mariette (2014) Grenier et al. (2016)

Lee et al. (2012) Guggisberg et al. (2013) Schuetz et al. (2013) Huc, Challois, Monziols, Michon, and Mariette (2014a) Guggisberg et al. (2015) Schuetz et al. (2016) Kraggerud et al. (2009)

Jeliński et al. (2007)

Everard et al. (2005) Everard et al. (2007)

Caccamo et al. (2004)

Wang and Sun (2004b)

Wang and Sun (2004a)

References

T. Lei and D.-W. Sun

Trends in Food Science & Technology 88 (2019) 527–542

Chemicals

Chemicals

Chemicals and maturity

Chemicals

Chemicals

Chemicals

Chemicals

Texture Sensory

Chemicals Chemicals Chemicals

Sensory Chemicals

Chemicals

Emmental cheese

Emmental cheese

Cheese (Soft)

Unhomogenized and Homogenized cheese curd Cheese

Fresh and freeze-dried cheese

Processed cheese

Zamorano cheese (Hard) Cheese

Zamorano cheese (Hard) Edam cheese Cheese (Hard)

Norvegia cheese (Semi-hard) Ricotta cheese

Cheddar cheese

Cheddar cheese

Molecular change and rheology Sensory and maturity

Cantal-type cheese

Cantal-type cheese

Cantal-type cheese

Classification of adulteration Molecular change and meltability Processing parameters

532 NIR

NIR NIR

NIR NIR NIR

NIR NIR

NIR

NIR

NIR

NIR

NIR

NIR

NIR

NIR

SF

SF

SF

SF

SF

Maturity

Cheese (Hard)

SF SF

Chemicals Meltability

Fench blue cheese Raclette cheese (Semi-hard); Comté cheese (Hard) Cheddar cheese

Nondestructive techniques

Attributes

Types of Cheese

Table 1 (continued)

MSC, SNV, DT, 1st Derivative, 2nd Derivative MSC Centering, Nomralisation, MSC Baseline correction, Normalisation, MSC, SNV, 1st SG

SNV, DT, 1st SG, 2nd SG

MSC, SNV, DT Centering, MSC, SNV, DT

2nd SG

MSC, SNV, DT, 1st Derivative, 2nd Derivative MSC, SNV, DT, 1st Derivative, 2nd Derivative

2nd Derivative

Maximum normalisation, 1st SG Maximum normalisation, 1st SG Maximum normalisation, 1st SG

MSC, 1st SG, 2nd SG

Centering

Centering, Normalisation, autoscale

Pre-processing Methods

PCA, PLSR

PCA, PLSR, ANOVA PCA, PLSR

PLSR PLSR PCA, MPLSR

PCA, MPLSR PCA, MPLSR

PLSR

PCA, MPLSR

MPLSR

PLSR

PLSR

PLSR

PLSR

PCA, PLSR

ICA, CCA

ICA, CCSWA

PCA-LDA, SPA-LDA, MLR ICA

PCA, PLSR PCA, CCSWA

Mathematical Models

Zhao et al. (2015)

(R2) Trans fatty acids < 0.65

(continued on next page)

Kraggerud et al. (2014) Madalozzo et al. (2015)

Oca et al. (2012) Mlček et al. (2013) González-Martín et al. (2014)

Revilla et al. (2009) González-Martín et al. (2011)

Pi et al. (2009)

Lucas, Andueza, Ferlay, and Martin (2008)

González-Martín et al. (2008)

Karoui, Mouazen, Dufour, Schoonheydt, and De Baerdemaeker (2006c) Sultaneh and Rohm (2007)

Karoui et al. (2006b)

Karoui et al. (2006a)

Downey et al. (2005)

(R) Sensory = 0.36, Amino N = 0.74, Lactic acid = 0.71 (R) Fat = 0.936, Moisture = 0.885, Protein = 0.757

(R ) Saturated fatty acid = 0.89 (fresh cheese), 0.81(Freezedried cheese); Trans fatty acid = 0.92 (fresh cheese), 0.84(Freeze-dried cheese) (R2) Fat = 0.962 (Without film), 0.941(With film); Protein = 0.933 (Without film), 0.908 (With film); NaCl = 0.892 (Without film), 0.861 (With film) (R) Texture = 0.961 (R2) Holes = 0.796, Hardness = 0.89, Chewiness = 0.838, Salty = 0.568, etc. (R2) Fat = 0.962, DM = 0.960, Protein = 0.978 (R) Free amino acids = 0.733–0.983 (R) Volatile compounds = 0.606–0.903

2

(R2) TS = 0.994(Unhomogenized curds), 0.997(Homogenized curds); Protein = 0.985(Unhomogenized curds), 0.992(Homogenized curds) (R) Fat = 0.97, Moisture = 0.96, Protein = 0.78, NaCl = 0.89

(R) Crumbly = 0.87, Rubbery = 0.83, Chewiness = 0.85, Mouthcoating = 0.78, Massforming = 0.8, Age = 0.97 (R2) Fat = 0.94, NaCl = 0.5, pH = 0.4, NPN = 0.85, TN = 0.86, WSN = 0.88 (R2) Fat = 0.94, NaCl = 0.5, pH = 0.4, NPN = 0.85, TN = 0.86, WSN = 0.88 (R2) Fat = 0.94, DM = 0.91, pH = 0.84, TN = 0.91, WSN = 0.95, WSN/TN = 0.61

(R2) Rheology = 0.34–0.99

Loudiyi, Rutledge, and AïtKaddour (2018b) Loudiyi et al. (2018a)

Loudiyi et al. (2017)

Dankowska et al. (2015)

Kokawa et al. (2015)

Abbas et al. (2012) Boubellouta and Dufour (2012)

(R2) Ash = 0.90, Protein = 0.81 (SECV) Ripening time = 0.93, Proteolysis index = 0.79, Free amino acids = 0.9 (RMSECV) Level of adulteration = 1.5–1.8%, Classification error = 0–3.8%

References

Results

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Trends in Food Science & Technology 88 (2019) 527–542

Chemicals

Maturity Texture and meltability

Sensory and texture

Maturity and sensory Chemicals

Classification of milk and whey Classification of geographical origins

Camembert-type cheese (Soft)

Camembert-type cheese (Soft) Process cheese

Process cheese

Cheddar cheese Swiss cheese

Cheese (Soft)

533 FTIR FTIR FTIR

Classification of brand Classification of PDO

Processing parameters

Chemicals and sensory Chemicals

Molecular structure Processing parameters

Chemicals

Sensory Chemicals Texture and meltability

Dielectric properties

Norvegia cheese (Semi-hard) Cheddar cheese

Cantal-type cheese Cantal-type cheese

Cheese (Soft)

Norvegia cheese (Semi-hard) Cheese (Process) Cheese (Process)

Tronchon cheese

DS

NIR+FTIR+FF DS DS

NIR+FTIR

FTIR FTIR

FTIR FTIR

FTIR

Meltability

FTIR

FTIR

FTIR FTIR

FTIR

FTIR FTIR

FTIR

FTIR

Raclette cheese (Semi-hard) and Comté cheese (Hard) Emmental cheese Parmigiano Reggiano cheese (Hard) Cheese

Pecorino cheeses (Semi-hard)

Chemicals

Swiss cheese

FTIR FTIR

FTIR

Classification of geographic origin Rheology Chemicals

Cheese (Soft) Cheese (Soft)

NIR FT-NIR

Maturity Classification of PDO

PDO cheese Parmigiano Reggiano cheese (Hard) Hard cheese

Nondestructive techniques

Attributes

Types of Cheese

Table 1 (continued)

Centering, Probabilistic quotient normalisation, SNV Maximum normalisation, 1st SG Normalisation

Baseline correction, Normalisation, MSC, SNV, 1st SG

SNV

Normalisation 1st SG

Normalisation

Normalisation

MSC, 1st SG, 2nd SG 2nd SG

MSC, 1st SG, 2nd SG

Base line correction MSC, 1st SG, 2nd SG

Base line correction, 1st SG, 2nd SG

Baseline correction Maximum normalisation, 1st SG

Normalisation

MSC, SNV, 1st Derivative MSC, 1st SG

Pre-processing Methods

MLR

PCA, PLSR, ANOVA PLSR PLSR, MLP-ANN

PLSR

ICA, ANOVA ICA, CCSWA

PCA, PLSR, ANOVA PCA, PLSR

MPLSR

PCA, CCSWA PCA, SIMCA, MLP-ANN

PCA, CCSWA

LDA

CCSWA

PCA, PLSR PLSR

PLSR

PCA, PLSR, ANOVA PCA, PLSR

PCA, PLSR

PLSR, SIMCA

PCA, Factorial Discriminant Analysis CCSWA PLSR

MPLSR, ANOVA PCA, SIMCA, MLP-ANN

Mathematical Models

(R2) Fat = 0.91, DM = 0.90, pH = 0.89, TN = 0.85, WSN = 0.89, WSN/TN = 0.6 (R) Sensory = 0.45, Amino N = 0.86, Lactic acid = 0.88 (R) Moisture = 0.94, Inorganic salt = 0.92 (R2) Melting temperature = 0.886, Melt diameter = 0.825, Hardness = 0.891 (R2) Water molecules = 0.985, Water flux = 0.987, Ions molecules = 0.998, Sodium chloride flux = 0.986

(R) Sensory = 0.44, Amino N = 0.86, Lactic acid = 0.89 (R2) Trans fatty acids < 0.65

(Inertia) Brand classification = 15–30% Classification success rate = 55–99% (SIMCA), 93.2–100% (ANN) (R2) Cheese yield = 0.69–0.95, Nutrient recovery = 0.49–0.88

Classification success rate > 95% (Ripening time, Manufacturing)

(R2) Ripening time = 0.97 (Under-rind), 0.98(Core) (R2) Hardness and springiness = 0.77, Cohesiveness = 0.81, Olson and price meltability = 0.88, Computer vision meltability = 0.64 (R) Hardness = 0.94, Rubbery = 0.95, Chewiness = 0.94, Creamy = 0.95, Mouthcoating = 0.85, Massforming = 0.83, etc. (R) Age = 0.84, WSN = 0.79, Sensory = 0.42–0.87 (R) Acetic acid = 0.95, Propionic acid = 0.91, Butyric acid = 0.92

(R2) pH = 0.84, DM = 0.91, ASN = 0.84, NPN = 0.92, NH4+ = 00.99, Lactose = 0.85, Lactic acid = 0.9

(Inertia) Brand classification = 74–95% (R2) Fat = 0.83, DM = 0.87, pH = 0.84, TN = 0.76, WSN = 0.79, WSN/TN = 0.6 (R) Fat = 0.98, Moisture = 0.96, Protein = 0.96

Currò et al. (2017) Cevoli et al. (2013)

(R2) TN = 0.95, WSN = 0.85, WSN/TN = 0.91 Classification success rate = 55–100% (SIMCA), 95.5–100% (ANN) Classification success rate = 52%

(continued on next page)

Kraggerud et al. (2014) Everard et al. (2006) Amamcharla and Metzger (2015) Velázquez-Varela et al. (2018)

Karoui et al. (2006c)

Loudiyi and Aït-Kaddour (2018) Loudiyi et al. (2018b)

Ferragina, Cipolat-Gotet, Cecchinato, and Bittante (2013) Kraggerud et al. (2014) Zhao et al. (2015)

Botosoa and Karoui (2013) Cevoli et al. (2013)

Lerma-García, Gori, Cerretani, Simó-Alfonso, and Caboni (2010) Boubellouta and Dufour (2012)

Kulmyrzaev and Dufour (2010)

Fagan et al. (2007c) Koca et al. (2007)

Fagan et al. (2007b)

Rodriguez-Saona, Koca, Harper, and Alvarez (2006) Martín-del-Campo, Picque, Cosio-Ramirez, and Corrieu (2007a) Martín-del-Campo et al. (2007b) Fagan et al. (2007a)

Kulmyrzaev et al. (2005) Karoui et al. (2006c)

Karoui et al. (2005)

References

Results

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Classification of adulteration Processing parameters Processing parameters

Spreadable cheese

534

Ultrasonic Acoustic Ultrasonic tap-test acoustic Ultrasonic

Crack Crack Eye and crack Eye and crack Coagulation

Classification of PDO

Oxygen

Blue cheese

Cheddar cheese

Polynomial fit, 2nd Derivative

Baseline correction, Normalisation, MSC, SNV, 1st SG Baseline offset correction, SG smoothing, Second polynomial DT Moving average smooth, Hilbert transform

SG smoothing 2nd SG

Cubrowser algorithm, 1st SG, 2nd SG

1st Derivative 1st Derivative, 2nd Derivative

1st SG

Pre-processing Methods

PCA, PLSDA

ANOVA

PCA PCA

T-test

PCA

PCA, PLSR

PLSR PLSR, ANN

PLSR, FDA

MLR GLM, ANOVA

PCA, PLSR, PLSDA

Mathematical Models

Classification success rate Bleu = 95.30 (d’Auvergne blue cheese), 89.60% (Fourme d’Ambert blue cheese)

Classification error = 0–10%

O’Mahony, O’Riordan, Papkovskaia, Kerry, and Papkovsky (2006)

Conde et al. (2008). Conde et al. (2008). Nassar et al. (2010) Nassar et al. (2010) Derra, Bakkali, Amghar, and Sahsah (2018) Kulmyrzaev et al. (2008)

Eskelinen et al. (2007)

Smith et al. (2017)

Zhao et al. (2015)

(R2) Trans fatty acids < 0.65 Limit of detection = 1%

Barreto et al. (2018) Vásquez et al. (2018)

Darnay, Králik, Oros, Koncz, and Firtha (2017)

Fagan et al. (2008) Nicolau et al. (2015)

de Sá Oliveira et al. (2016)

References

(R2) Fat = 0.979, Hardness = 0.846(Treated), 0.944(No treated); Classification error (%) = 12 (Linear FDA), 15 (Quadratic) (R2) Starch = 0.992 (Full model), 0.832 (Reduced model) (R2) Hardness = 0.8735–0.967(PLSR), 0.8833–0.967 (ANN)

(R) Starch = 0.984, Classification success rate = 100.0% (Without starch samples) (R2) Fat losses = 0.93, Curd yield = 0.90, Moisture = 0.94 R2 = 0.977–0.997

Results

Note: ANN = artificial neural network, MLP-ANN = Multilayer perceptron-ANN, ANOVA = analysis of variance, ASN = acid-soluble nitrogen, CCA = canonical correlation analysis, CCSWA = components and specific weights analysis, CT = computed tomography, CV = computer vision, DM = dry matter, DS = dielectric spectroscopy, DT = detrending, FDA = Fisher's discriminant analysis, FF = front-face fluorescence spectroscopy, FTIR = Fourier transform infrared spectroscopy, GLM = general linear model, HT = Hough transform, ICA = Independent components analysis, KR = Kraggerud's method, LB = light backscatter, LDA = linear discriminant analysis, MI = multispectral imaging, MLR = multiple linear regression, MRI = magnetic resonance imaging, MSC = multiplicative scatter correction, NIR = near-infrared spectroscopy, NPN = non-protein nitrogen, FT-NIR = Fourier transform near-infrared spectroscopy, PCA = principal component analysis, PDO = protected designation of origin, PLSDA = partial least squares discriminate analysis, PLSR = partial least squares regression, MPLSR = modified-PLSR, R = correlation coefficient, R2 = determination coefficient, RMSECV = root mean square error of cross validation, SECV = standard error of cross validation, SF = synchronous fluorescence spectroscopy, SIMCA = soft independent modelling of class analogy, SNV = standard normal variate, SG = Savitzky–Golay. TN = total nitrogen, TS = Total solid WSN = water-soluble nitrogen.

Optical oxygen analyser

MI

Ultrasonic

Eye and crack

Finnish Emmental (Swisstype) cheese Manchego Cheese Manchego Cheese Cheese Cheese Cheese

Raman imaging

Chemicals

Distribution of components

Raman imaging

Chemicals Texture

Fresh cheese Swiss-type cheese (Semihard) Cheddar cheese

Swiss cheese (Semi-hard)

HSI HSI

Chemicals, texture and Classification of enzyme

HSI

LB LB

Raman spectroscopy

Nondestructive techniques

Cheese (Semi-hard)

Other Nondestructive Techniques

Cheese Sheep cheese

Attributes

Types of Cheese

Table 1 (continued)

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Fig. 2. (a) Images of a Cheddar cheese disc before and after cooking for evaluating oiling off (Wang & Sun, 2004a); (b) Comparison between inner structure of cheeses using CT and after cutting the cheeses (Lee et al., 2012); (c) X-ray image of cheese eyes (Kraggerud et al., 2009); (d) 3D MRI images of cheese at different ripening stages (horizontal axis) and frames (vertical axis) (Musse et al., 2014).

detecting chemical components in cheese are scarce due to their principles (Table 1). Besides, the main issue in applying these techniques is the cost, limiting their use for routine control of eye formation in commercial cheese factories (Schuetz et al., 2013).

2018a). Therefore, most of recent literatures use SF spectroscopy rather than FF spectroscopy to achieve better results for cheese applications (Table 1). Adulterated cheese samples exhibit differences in fluorescence spectra due to the differences in chemical compositions (Dankowska et al., 2015; Ozbekova & Kulmyrzaev, 2017). This characteristic makes it possible to discriminate various cheeses and evaluate authenticity based on fluorescent fingerprints. The potential of SF spectroscopy for the discrimination of cheese adulterated with plant oils was investigated with the lowest classification error rates equal to 3.8% (Δλ = 10 and 30 nm) and 0.0% (Δλ = 60 nm) for the principal component analysis-linear discriminate analysis (PCA–LDA) and successive projections algorithm (SPA)–LDA classification models, respectively (Dankowska et al., 2015). FF spectroscopy coupled with common components and specific weights analysis (CCSWA) were successfully used to discriminate various soft cheeses based on the attributes of corresponding raw milk and whey (Kulmyrzaev & Dufour, 2010). FF spectroscopy is also used to classify cheeses according to their brands (Botosoa & Karoui, 2013), geographic origins (Karoui et al., 2004a; 2004b, 2005), manufacturing processes and sampling zones (Karoui et al., 2007). It should be noted that the ability of classification is significantly affected by selected fluorescent compounds, as Karoui et al. (2004a, 2004b) showed that vitamin A had better classification ability with successful rate of 90.5% than tryptophan with successful rate of 44.2%. In evaluating cheese chemical attributes, SF spectroscopy coupled with independent components analysis (ICA) can delineate the molecular structure changes of Cantal cheese under the effect of different salt contents (KCl and NaCl) during heating or cooling (Loudiyi et al.; 2017, 2018a). Abbas, Karoui, and Aït-Kaddour (2012) succeeded in using SF spectroscopy to predict ash and protein in French blue cheese

4. Spectroscopic techniques 4.1. Fluorescence spectroscopy Fluorescence spectroscopy is a highly sensitive, rapid, nondestructive and easy to use analytical technique that provides information on the presence of fluorescent molecules. Natural compounds that present fluorescence in cheese are aromatic amino acids, vitamin A and tryptophan, which can provide specific information on the physical state of triglycerides, protein–lipid interactions, and protein conformational changes (Abbas et al., 2012; Kulmyrzaev et al., 2005; Ozbekova & Kulmyrzaev, 2017). Classic front-face fluorescence (FF) spectroscopy is always applied after fixing the excitation or the emission wavelengths of some intrinsic probes. As cheese is a nonisotropic and complex matter presenting different physicochemical properties, the use of a few excitation or emission wavelengths for the excitation restricts the ability of efficiently predicting some physicochemical parameters in cheeses (Abbas et al., 2012), and therefore synchronous fluorescence (SF) spectroscopy with simultaneous use of different excitation wavelengths was developed. SF spectroscopy scan simultaneously both excitation (λex) and emission wavelength (λem) with a constant interval (Δλ = λex−λem), taking into account the whole fluorescence landscape with reservation of information related to several fluorophores (Abbas et al., 2012; Boubellouta & Dufour, 2012; Dankowska et al., 2015; Loudiyi et al., 535

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with the best results of R2 = 0.90 and R2 = 0.81, respectively, Ozbekova and Kulmyrzaev (2017) predicted moisture, protein and fat contents in Tilsit cheese with R2 of 0.99, 0.98 and 0.98, respectively, while Kraggerud et al. (2014) evaluated lactic acid and Amino N with R values of 0.72 and 0.84, respectively. For cheese physical and heat-induced attributes, Ozbekova and Kulmyrzaev (2017) employed FF spectroscopy to predict the rheology and melting temperature of Tilsit cheese with R2 = 0.62–0.90 and R2 = 0.99, respectively, Loudiyi et al. (2018a) obtained the rheological attributes of Cantal-type cheeses with R2 of 0.34–0.99, Boubellouta and Dufour (2012) predicted melting temperature of cheese using SF spectroscopy with results similar to those obtained by reference method. For predicting sensory characteristics of Norvegia cheese, the result was under expectation with R value of 0.39 (Kraggerud et al., 2014). Partial least-squares regression (PLSR) models that predict the maturation time, proteolysis index and free amino acids from the FF data were constructed with the results of R2 of 0.93, 0.79, and 0.90, respectively (Kokawa et al., 2015). Due to the fingerprinting nature, fluorescence spectroscopy is very suitable for the classification of cheese products based on different attributes (Fig. 3), with results normally better than other techniques such as MIR spectroscopy (Karoui et al.; 2004a, 2005).

widely used in cheese analysis (Woodcock et al., 2008), while some components such as moisture in cheese are more accurately measured by NIR transmittance spectroscopy (McKenna, 2001). There are many literatures related to the use of NIR spectroscopy for predicting chemical components in cheese. All results of predicting fat and most results of predicting protein and moisture/dry matter (DM) in different types of cheeses and curds can be considered as excellence (R2 > 0.91) (Karoui et al., 2006a, 2006b, 2006c; Sultaneh & Rohm, 2007; González-Martín, González-Pérez, Hernández-Hierro, & González-Cabrera, 2008; Oca, Ortiz, Sarabia, Gredilla, & Delgado, 2012; Madalozzo, Sauer, & Nagata, 2015). Moreover, fat, protein and NaCl in cheese slice wrapped with a polyethylene (PE) film were also successfully determined with R2 of 0.941, 0.908 and 0.861, respectively (Pi et al., 2009). However, prediction results of chemicals with small amount in various types of cheese were not stable with R2 ranging from 0.606 to 0.903 for volatile compounds (González-Martín et al., 2014), from 0.5 to 0.89 for NaCl (González-Martín et al., 2008; Karoui et al., 2006a), from 0.46 to 0.92 for fatty acids (Lucas et al., 2008), from 0.61 to 0.88 for WSN (Karoui et al., 2006a; 2006b; 2006c), from 0.733 to 0.983 for free amino acids (Mlček et al., 2013), and R2 < 0.65 (failure prediction) for trans fatty acids (Zhao et al., 2015). NIR spectroscopy was also used to successfully predict ripeness (WSN/TN) with R2 of 0.91 (Currò et al., 2017), Cheddar cheese age with R of 0.97 (Downey et al., 2005), and Zamorano cheese texture with R of 0.961 and residual prediction deviation (RPD) of 5.4 (Revilla et al., 2009). However, previous studies (Downey et al., 2005; González-Martín et al., 2011; Kraggerud et al., 2014) in predicting most sensory attributes of cheese did not show good results (R2 < 0.81) as only R2 > 0.81 can be considered as good prediction. In addition, conventional NIR spectroscopy is normally not used for classification, and only one study focused on cheese classification using NIR wavelength range based on FT-NIR spectroscopy (Cevoli et al., 2013). The above studies confirm that NIR spectroscopy is broadly utilized in predicting chemical components in cheese, and the most used

4.2. Near-infrared spectroscopy Near infrared (NIR) spectroscopy is a powerfully nondestructive technique that is routinely applied to predict food quality (Currò, Manuelian, Penasa, Cassandro, & De Marchi, 2017; González-Martín et al., 2011; Morsy and Sun, 2013; He and Sun, 2015; Wang et al., 2017a, 2017b; Xu et al., 2015). It provides complex structural information (Fig. 4) related to the vibration behaviour of combinations of chemical bonds in the wavelength ranging from 780 to 2500 nm (Cen & He, 2007; Cevoli et al., 2013). Commonly used NIR spectroscopy has two modes: reflectance and transmittance. Reflectance mode is more

Fig. 3. Number of publications on nondestructive techniques used for different cheese attributes between 2004 and 2018. 536

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Fig. 4. (a) Cheese NIR spectrum (González-Martín et al., 2008); (b) FTIR spectrum (Fagan et al., 2007c).

Fig. 5. Frequency of using nondestructive techniques for cheese quality evaluation between 2004 and 2018.

chemometric method is PLSR, which appears in 94.44% relevant literatures. The amount of chemicals can affect the accuracy of prediction, NIR spectroscopy is hence very suitable for predicting major chemical components such as fat, protein and moisture but it is not stable for predicting chemicals with small amount in cheese products. NIR has been widely used in the industry because it has high spectral stabilities (Cen & He, 2007) and most packaging materials are transparent to NIR light (Oca et al., 2012). However, literatures related to NIR spectroscopy for cheese quality evaluation become less in recent years (Fig. 5) because applications on evaluating other attributes are inadequate (Fig. 3) and innovative chemometric methods are not used in research. For these reasons, future study of NIR spectroscopy should focus on broadening the range of applications and developing more advanced chemometric methods.

Harper, & Rodriguez‐Saona, 2009b). FTIR spectroscopy is the preferred method of mid-infrared (MIR) spectroscopy (Woodcock et al., 2008), and thus all MIR spectra in previous researches for nondestructive quality evaluation of cheese were obtained by FTIR spectroscopy (Table 1). The wavenumber range of MIR is between 4000 and 700 cm−1, which provides information by showing distinctive narrow bands (Fig. 4) arising from the vibrations of functional groups of the molecules and correlative rotational–vibrational structure (Cevoli et al., 2013; Karoui et al., 2006a; Martín-del-Campo et al., 2007a; RodriguezSaona et al., 2006). It should be noted that samples, especially low-moisture products, might require dilution in some medium due to relatively high absorption coefficients exhibited by many components in MIR region, which limits the application of FTIR for industrial use. In some literatures, researchers use water-soluble fraction of cheese (Karoui et al., 2004b, 2006a, 2004a; Subramanian et al., 2009b, 2011, 2009a; Margolies & Barbano, 2018) as measuring water-soluble fraction improved prediction ability for chemical components with small amount such as fatty acids (Koca et al., 2007) and WSN (Karoui et al., 2006a), but prediction ability for fat was reduced (Karoui et al., 2006a), therefore these studies should not be considered as applications of nondestructive techniques. Similar to NIR spectroscopy, FTIR spectroscopy is widely used for predicting chemical components in cheese (Fig. 3). Comparing with NIR spectroscopy, FTIR spectroscopy has the same good ability for

4.3. Fourier transform infrared spectroscopy Fourier transform infrared (FTIR) spectroscopy is an attractive technique that has been largely researched and applied for reliable, rapid and real-time evaluation of cheese products without special skills of the users (Koca, Rodriguez-Saona, Harper, & Alvarez, 2007; Fagan et al., 2007b; Martín-del-Campo, Picque, Cosio-Ramirez, & Corrieu, 2007b). It is based on the principle that different functional groups require different amounts of energy for excitation (Subramanian, 537

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predicting moisture/DM (R2 = 0.87 to 0.92) and protein content (R2 = 0.76 to 0.92) in cheeses, however, predicted results for fat content (R2 = 0.83 to 0.96) are not as good as those by NIR spectroscopy (Karoui et al., 2006c; Martín-del-Campo et al., 2007a; Rodriguez-Saona et al., 2006). As for the determination of chemical components with small amount, WSN was predicted with R2 of 0.62 for process cheese (Fagan et al., 2007c) and 0.79 for soft cheese (Karoui et al., 2006c), while acid-soluble nitrogen (ASN), non-protein nitrogen (NPN), NH4+, lactose and lactic acid in Camembert-type cheese were predicted with R2 of 0.84, 0.92, 0.99, 0.85 and 0.9, respectively (Martín-del-Campo et al., 2007a), short-chain free fatty acids in Swiss cheese were predicted with R ranging from 0.91 to 0.98 (Koca et al., 2007), and pH was predicted with R2 of 0.84 (Karoui et al., 2006c; Martín-del-Campo et al., 2007a). For evaluating processing attributes of cheese, age was successfully predicted with R2 of 0.88 for Cheddar cheese (Fagan et al., 2007c) and R2 > 0.97 for Camembert-type cheese (Martín-del-Campo et al., 2007b), WSN/TN was predicted with R2 of 0.6 (Karoui et al., 2006c), cheese yields and nutrition recoveries were predicted with R2 of 0.69–0.95 and 0.49–0.88, respectively (Ferragina et al., 2013). Sensory is a complex attribute of cheese, which comprises of many parameters, resulting in unstable results predicted by FTIR spectroscopy. Only models for predicting some of the sensory parameters including firmness, rubbery, creamy, chewy and fragmentable obtained good results (Fagan et al.; 2007b, 2007c). Fagan et al. (2007a) evaluated texture properties with R2 ranging from 0.77 to 0.81 and Olson and Price meltability with R2 of 0.88, while structure at molecular level of cheeses was investigated by Loudiyi and Aït-Kaddour (2018). FTIR spectroscopy can be employed for classifying cheese products. Cevoli et al. (2013) estimated the PDO cheese authenticity with corrected classification rate > 93.2% and the average probability of correct identification was 0.974 using artificial neural network (ANN), while Karoui, Bosset, Mazerolles, Kulmyrzaev, and Dufour (2005) obtained normal result (52%) for the classification of hard cheese based on different geographic origins using factorial discriminant analysis. Besides, FTIR spectroscopy coupled with LDA was utilized to discriminate Italian Pecorino cheeses according to cheese age and manufacturing technique with accuracy > 95% (Lerma-García et al., 2010). The studies discussed above demonstrate that FTIR spectroscopy in MIR wavelength range is a suitable method for quality evaluation of cheese products with wide applicability in scientific research. Because of the nature of FTIR, it allows for not only the prediction of a variety of chemical bonds but also the chemically based discrimination of organic components (Cecchinato et al., 2015; Koca et al., 2007; RodriguezSaona et al., 2006; Subramanian et al., 2009a). FTIR has well documented wavelengths for chemical components in cheese and thus has relatively higher accuracy comparing with NIR. However, the way of spectral acquisition, which generally needs the assistance of integrating sphere, limits the use of FTIR spectroscopy in the industry. Table 1 and Fig. 3 show that FTIR is the most widely used nondestructive technique for evaluating almost all cheese attributes except heat-induced properties and eyes and cracks. The prediction reported in all relevant studies uses PLSR regression models. However, FTIR cannot evaluate packaged cheese products since the radiation light of MIR has a very short penetration depth, and sufficient comparison between different classification algorithms is not available.

dielectric loss factors, respectively. Amamcharla and Metzger (2015) investigated the DS as a potential method to evaluate hardness, melting point, and modified Schreiber melt diameter of process cheese with R2 of 0.891, 0.92 and 0.831, respectively. Recently, Velázquez-Varela et al. (2018) coupled measurements of both dielectric constant and conductivity to predict the chemical components involved in cheese salting process, and its structural changes, achieving R2 values of 0.985 for number of water molecules, 0.9867 for water flux, 0.9981 for number of ions molecules, and 0.9859 for sodium chloride flux). These studies suggest that dielectric spectroscopy has great performances for determining chemicals with small amount in cheese, but information available is still limited, and future studies should focus on applying dielectric spectroscopy to evaluate more quality attributes of cheese. In addition, Raman spectroscopy was used to discriminate adulterated spreadable cheese with 100% accuracy and 95% confidence and to predict starch content with R of 0.984 (de Sá Oliveira et al., 2016). Fagan, Castillo, O’Donnell, O’Callaghan, and Payne (2008) monitored some processing parameters of cheese using light backscatter, fat losses, yield and moisture content of curd were determined with R2 of 0.93, 0.90 and 0.94, respectively, and Nicolau, Buffa, O’callaghan, Guamis, and Castillo (2015) also predicted clotting time, cutting time and rheology of sheep cheese using light backscatter with R2 of 0.997, 0.977 and 0.993, respectively. Color measurements using spectrocolorimeter (Dufossé et al., 2005) and fibre-optic sensor (Everard et al., 2009) are also available for cheese process control. However, color parameters are normally considered as indicators for target cheese attributes, but these parameters can only reflect surface information of cheese and are not as comprehensive as NIR or FTIR spectra. 5. Other nondestructive techniques 5.1. Hyperspectral imaging As both imaging and spectroscopic techniques are integrated in a single system, hyperspectral imaging (HSI) enables to simultaneously acquire spectral and spatial information for the analytical purpose (Barreto et al., 2018). The systems acquire images with an abundance of contiguous wavelengths, and the interval is normally less than 10 nm (Wu & Sun, 2013). The final data acquired by HSI are called “hypercube” since they can be demonstrated as containing three dimensions with two for spatial information and the other one for spectral information (Feng & Sun, 2012). HSI is a chemical-free nondestructive assessment method that requires minimal sample preparation, and thus has been widely studied for food quality and safety evaluation (Liu et al. 2017, 2018; Ma et al. 2017, 2018; Pan et al. 2018; Cheng et al. 2016a, 2016b, 2017, 2018; Dai et al. 2016). For HSI in dairy research, recent literatures focused on detecting melamine in powdery milk (Fu et al., 2014; Huang et al., 2016) and classification of milk powders (Munir, Wilson, Yu, & Young, 2018). Cheese quality evaluation also started to attract researchers’ interest in the past two years (Fig. 5). HSI was found to be suitable to predict fat content and hardness of semi-hard cheese through packaging during ripening with high R2 of 0.979 and 0.944, respectively; and cheese samples with enzyme treatment and without treatment were also successfully classified with 12% error (Darnay et al., 2017). Vásquez et al. (2018) revealed that both PLSR and ANN had the ability to determine hardness of Swiss-type cheese during ripening with R2 between 0.89 and 0.97. A R2 value of 0.9915 and RMSECV of 0.3979 were obtained for modelling starch content in adulterated fresh cheese, while R2 of 0.8321 and RMSEP of 1.3515 were obtained for a reduced model (Barreto et al., 2018). Therefore, HSI has the potential for evaluating both chemical and physical attributes of cheese. However, HSI application in quality evaluation for cheese still lacks, and further research should be carried out in the future.

4.4. Other spectroscopic techniques Dielectric spectroscopy (DS) is also available for providing nondestructive determination of cheese quality attributes. DS describes the interaction between microwaves with a matter (Amamcharla & Metzger, 2015). Everard, Fagan, O’donnell, O’callaghan, and Lyng (2006) successfully predicted moisture (R = 0.94) and inorganic salt content (R = 0.92) in process cheese using dielectric constants and 538

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5.2. Raman imaging

6. Conclusions and future trends

Raman imaging is a technique integrating Raman spectroscopy with imaging, and it has recently been widely investigated for the detection of harmful residues or contaminates for ensuring food safety (Jiang et al., 2018, 2019; Yaseen et al., 2017; 2018a, 2018b, 2019; Fu et al., 2019; Wang et al., 2018, 2019; Pan et al., 2017a, 2017b, 2018a, 2018b; Pu et al., 2017; Li et al., 2017). It is also effective for imaging components in cheese samples and providing abundant spectral information (de Sá Oliveira et al., 2016; Smith et al., 2017). Smith, Holroyd, Reid, and Gordon (2017) revealed that fat, protein, moisture, trisodium citrate, paprika and starch distributions of process cheese could be effectively imaged using Raman imaging technique. Qin, Chao, and Kim (2013) investigated Raman imaging for simultaneously identifying various adulterants in milk powder. Although Raman imaging technique has very high accuracy for cheese quality evaluation, it is not yet employed in as a standard in the laboratory and the industry. The food industry mainly relies on NIR spectroscopy because it is cheaper and simpler (de Sá Oliveira et al., 2016). Table 1 also shows studies available on evaluating cheese quality by Raman imaging.

With continuous demand for safety and quality of cheese, researches on nondestructive quality evaluation of cheese products are rapidly evolving and developing. In the current review, literatures related to cheese quality evaluation using imaging, spectroscopic and other nondestructive techniques are summarized. The importance of evaluating cheese quality attributes is presented, and the main quality attributes of cheese and their most used measurement methods are highlighted. Principles, trends of development, application scenarios and the pros and cons of employing these nondestructive techniques are discussed based on results in relevant literatures. Fluorescence spectroscopy is the most used technique for classifying geographic origin and monitoring manufacturing process, NIR spectroscopy is mainly used for determining cheese chemical components in industrial field due to its stability and penetrability, FTIR spectroscopy has the most extensive scope of applications in scientific research, but the use of FTIR in the industry is limited because inert medium might be required for testing, while CV, X-ray and MRI are specifically used for monitoring eye growth, and ultrasonic and acoustic sensing are also frequently used for the same purpose. Research trends of these techniques for the cheese industry include that HSI and Raman imaging start to attract research interest, front-face fluorescence spectroscopy is gradually substituted by synchronous fluorescence spectroscopy, whereas CV is no longer a research hotspot in cheese quality evaluation and literatures related to NIR spectroscopy also becomes less in the past few years. Therefore, future studies on nondestructive techniques for evaluating quality attributes of cheeses may be suggested as follows:

5.3. Ultrasonic and acoustic sensing Like CT, X-ray and MRI, ultrasonic and acoustic techniques have been widely used to monitor eyes and cracks in cheese in the past 15 years (Table 1). Ultrasonic and acoustic sensing devices provide nondestructive, quick and low-cost measurements. Eskelinen, Alavuotunki, Hæggström, and Alatossava (2007) indicated that ultrasonic method was able to monitor eyes and cracks of the cheese during the maturity, and 3-D ultrasound images of cheese sample are reconstructed. Nassar et al. (2010) monitored cheese eye formation using ultrasonic method and revealed that limitations occurred in the use of ultrasound especially for mature cheeses, which present very high degrees of opening, and an alternative method called tap test acoustic technique was developed to solve this problem. Conde, Mulet, Clemente, and Benedito (2008) also illustrated that using ultrasonic pulse-echo test, it was not possible to detect cracks inside Manchego cheese due to the high porosity in this type of cheese that scattered the ultrasonic waves in the outermost layers, and an alternative way for this purpose was the use of the acoustic impulse-response technique with discrimination error of 0–10%. In addition to the monitoring of eyes and cracks, ultrasonic pulse-echo technique was used to monitor the milk coagulation after rennet addition in cheese manufacture (Derra et al., 2018).

(1) For conventional techniques such as computer vision, machine learning and deep learning can be coupled with this technique to enhance its evaluation ability and capability. In addition, computer vision can be used as an auxiliary method for other novel nondestructive techniques including HSI and multispectral imaging. (2) For techniques traditionally used in medical diagnostics including CT, X-ray and MRI, expensive equipment is the main issue, and thus simpler instrument with lower cost and higher efficiency should be developed in order to satisfy requirements of the cheese industry. (3) For relatively mature spectroscopic techniques like fluorescence, NIR and FTIR spectroscopies, more comparison between fluorescence and FTIR spectroscopies for classification and comparison between NIR and FTIR spectroscopies for prediction are needed, and innovative chemometrics should be introduced and developed for NIR and FTIR spectroscopies to enhance their prediction performance for cheese quality attributes, especially chemicals with small amount. The spectral acquisition technique of FTIR spectroscopy should be improved to meet the industrial needs. (4) For recently developed techniques such as Raman imaging, HSI and dielectric spectroscopy, more researches should be conducted to improve their performances and to evaluate more quality attributes from various cheeses to broaden their applicability.

5.4. Multispectral imaging and optical oxygen sensors Other techniques including multispectral imaging and optical oxygen sensors were also used for cheese quality evaluation. A multispectral image sensor captures image data at a number of specific frequencies across the electromagnetic spectrum, and the wavelengths are normally separated using filters or instruments that are sensitive to particular wavelengths. Kulmyrzaev, Bertrand, and Dufour (2008) using multispectral imaging technique to classify Bleu d’Auvergne and Fourme d’Ambert blue cheese with success rate of 95.30% and 89.60%, respectively. Optical oxygen sensor is developed based on the principle that dissolved oxygen quenches the luminescence that is associated with the chemical dyes used in the sensor. The sensor measures dissolved oxygen by emitting a blue light, and the luminescence signal decreases with increase in oxygen. Optical oxygen sensor was shown to provide meaningful information about performance of the packaging process, product storage conditions and Cheddar cheese quality in a fast, nondestructive and cost-efficient way (O'Mahony et al., 2006).

Acknowledgements Tong Lei would like to acknowledge University College Dublin (UCD) and China Scholarship Council (CSC, China) for financial support to his PhD study under the UCD-CSC funding scheme. References Abbas, K., Karoui, R., & Aït-Kaddour, A. (2012). Application of synchronous fluorescence spectroscopy for the determination of some chemical parameters in PDO French blue cheeses. European Food Research and Technology, 234(3), 457–465. Amamcharla, J. K., & Metzger, L. E. (2015). Prediction of process cheese instrumental texture and melting characteristics using dielectric spectroscopy and chemometrics. Journal of Dairy Science, 98(9), 6004–6013.

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