Next Article in Journal
Development of a Multifunctional Edible Coating and Its Preservation Effect on Sturgeon (Acipenser baeri× Acipenser schrenckii) Fillets during Refrigerated Storage at 4 °C
Next Article in Special Issue
A Genotyping Method for Detecting Foreign Buffalo Material in Mozzarella di Bufala Campana Cheese Using Allele-Specific- and Single-Tube Heminested-Polymerase Chain Reaction
Previous Article in Journal
The Protective Effects of Iron Free Lactoferrin on Lipopolysaccharide-Induced Intestinal Inflammatory Injury via Modulating the NF-κB/PPAR Signaling Pathway
Previous Article in Special Issue
Stepwise Approach for Tracing the Geographical Origins of the Manila Clam Ruditapes philippinarum Using Dual-Element Isotopes and Carbon Isotopes of Fatty Acids
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Authenticity and Typicity of Traditional Cheeses: A Review on Geographical Origin Authentication Methods

Department of Comparative Biomedicine and Food Science, University of Padova, Viale Università 16, 35020 Legnaro, PD, Italy
Univ Brest, INRAE, Laboratoire Universitaire de Biodiversité et Écologie Microbienne, F-29280 Plouzané, France
Author to whom correspondence should be addressed.
Foods 2022, 11(21), 3379;
Received: 3 October 2022 / Revised: 20 October 2022 / Accepted: 22 October 2022 / Published: 26 October 2022
(This article belongs to the Special Issue Recent Advances in Geographical Traceability of Food Products)


Food fraud, corresponding to any intentional action to deceive purchasers and gain an undue economical advantage, is estimated to result in a 10 to 65 billion US dollars/year economical cost worldwide. Dairy products, such as cheese, in particular cheeses with protected land- and tradition-related labels, have been listed as among the most impacted as consumers are ready to pay a premium price for traditional and typical products. In this context, efficient food authentication methods are needed to counteract current and emerging frauds. This review reports the available authentication methods, either chemical, physical, or DNA-based methods, currently used for origin authentication, highlighting their principle, reported application to cheese geographical origin authentication, performance, and respective advantages and limits. Isotope and elemental fingerprinting showed consistent accuracy in origin authentication. Other chemical and physical methods, such as near-infrared spectroscopy and nuclear magnetic resonance, require more studies and larger sampling to assess their discriminative power. Emerging DNA-based methods, such as metabarcoding, showed good potential for origin authentication. However, metagenomics, providing a more in-depth view of the cheese microbiota (up to the strain level), but also the combination of methods relying on different targets, can be of interest for this field.

Graphical Abstract

1. Introduction

The shared definition of food fraud relates to intentional illegal acts performed by food value chain operators for economic gain [1]. More specifically, in the framework of the European agri-food chain legislation, food fraud is defined as “any suspected intentional action by businesses or individuals for the purpose of deceiving purchasers and gaining undue advantage therefrom, in violation of the rules referred to in Article 1(2) of Regulation (EU) 2017/625” [2]. Behind the term “food fraud”, multiple practices designed to deceive purchasers, which are categorized under the following denominations: (i) substitution, (ii) concealment, (iii) dilution, (iv) unapproved enhancement, (v) counterfeit, (vi) grey market/forgery, and finally, (vii) mislabeling, exist. Substitution corresponds to total or partial replacement of a food, including ingredients or nutrients, with one of lower value. Concealment hides the low quality of food ingredients or food products. Dilution is self-explanatory and corresponds to the action of mixing a high-value ingredient with a lower one, while unapproved enhancement improves food quality by adding undeclared or unknown ingredients. These four food fraud types are grouped under the term “adulteration”. Counterfeit refers to the infringement of Intellectual Property Rights via replication of a product or its packaging, while grey market or forgery corresponds to production, theft, and diversion involving unauthorized sales of foodstuffs. The latter generally concerns products for which production agreements or quotas exist or geographical restrictions apply. Finally, mislabeling corresponds to distorted information or false claims on packaging or labels [3].
The global estimated value of food frauds each year ranges from 10 to 65 billion US dollars, without considering potentially related losses [1]. Indeed, unfair competition may not only result in economic losses for honest producers and retailers but could also impact food safety and quality, public health, and society at a large scale, thus impeding a perfectly accurate estimation of food fraud socio-economic impacts [4,5].
In recent years, dairy products have been systematically listed among the most common food frauds [6,7,8,9,10,11], with cheese being the most prevalent [11,12]. In this case, fraudulent documentation and adulteration/substitution were the most frequent events [12,13]. Between 2000 and 2018, the HorizonScan program (a subscription-based service monitoring global food integrity issues, including brand identity) reported 245 cases of dairy frauds, from which 51% were characterized by fraudulent documentation [12]. Similar findings were reported by Montgomery et al. [13], who stated that cheese fraudulent documentation accounted for 74% of total fraud cases (n = 98) between 2015 and 2019 [10,11]. In this context, actions against food fraud are taken by inspecting agencies, producers, and retailers [14,15,16,17]. Nevertheless, the increased complexity of a globalized supply chain can impair fraud incidents from being detected.
The term “authenticity” for food products is associated with the fact that there is a “match between the food product characteristics and the corresponding food product claims” [18]. In this context, cheeses are defined as ripened or unripened soft, semi-hard, hard, or extra-hard products, obtained by milk protein coagulation using rennet, other suitable coagulating agents, or processing technologies, and have whey protein/casein ratios that do not exceed that of milk [19]. Cheese quality is often linked to value descriptors such as environmental welfare standards, production methods, and safety claims, but also geographical origin [20].
As for the term “typicity”, it is defined by the unique combination of natural and human factors associated with a specific terroir [21]. Cheese typicity (i.e., the recognizable organoleptic traits associated with a given cheese) is acquired from the specific raw materials used, traditional tools, and the encountered environmental and production conditions, cheese-making process, and geographical area [22]. At the European level, linked to this typicity, certain cheeses can be recognized with distinctive labels, such as Protected Designation of Origin (PDO), which indicates that the products were entirely manufactured in a defined geographical area, Protected Geographical Indication (PGI), which correlates a geographical area with at least one of the product transformation steps, and Traditional Specialties Guaranteed (TGS), that highlights a traditional aspect for food products without any link to a specific geographical area. Other labels, such as “product of island farming” or “mountain product”, can also be found [23,24,25]. A strong societal demand currently exists for natural, local, traditional, and authentic foods. Authentic food quality is recognized by the consumer to have a higher added value, but this higher value increases the risk for fraud, in particular fraudulent documentation including omission or irregular use of geographical origin and failure to adopt suitable traceability systems (i.e., corresponding to mislabeling) [13,26]. At the European level, Regulation [27] established the implementation of a comprehensive traceability system within food businesses and required a suitable documentation system to identify the product along the food chain, while Regulation [28] (Art. 26) requires labeling “country of origin” for products such as Geographical Indication and meat or products, for which mislabeling would mislead the consumer. Moreover, the increasing consumer demand for natural, local, and traditional foods has led to national laws, such as in Italy and Spain, regulating how to label the geographical origin of milk and milk derivatives [29,30]. Accordingly, geographical origin, described as a “specific location” that serves to designate a product origin such as territory of a member, region, or locality in a given territory [31] (Art. 22), has crucial relevance in dairy products. The need for food authentication methods is driven by different actors. This includes producers and retailers for whom food fraud induces economical losses, public authorities that verify compliance with agri-food chain legislation, and finally, the consumer, to ensure trust when buying a product. While, in the past, authentication analyses focused on evaluating a single molecule or single parameters, nowadays, these methods are evolving from targeted to untargeted approaches. This enables the description of multiple product features and characteristics to provide a way to develop fingerprints for cheese geographical origin authentication. This is particularly relevant for protected land- and tradition-related labeled cheeses that are often incriminated in food frauds due to their high economical value.
Two different strategies can be employed to authenticate cheese origin. The first one involves exploring the relationship between the biological and/or chemical components, assuming that their proportions are constant for a particular cheese at a specific time during production or shelf-life. In this context, it seems clear that pattern recognition methods (e.g., such as principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA)) create unique classes, potentially differentiating typical and fraudulent products [32]. The second strategy aims to find specific chemical or biological components which can be used as markers for traditional cheese authentication (e.g., mass spectrometry analysis, such as stable isotope ratio and trace elements) that can all reflect cheese chemical composition. DNA-based methods for authentication are also emerging in the dairy sector, as shown recently by the work of Kamilari et al. [33], who used cheese microbiota metabarcoding for this purpose. However, for authentication of geographical origin, methods such as isotopic profiles [34] are generally preferred. As food labeling systems are constantly evolving, in parallel with legislation (e.g., Regulation [2]), analytical tests and technical control measures need to be improved and updated to counteract present and emerging fraud systems [35].
In this context, this review (based on 167 articles published over the last 27 years) aims to present methods, either physical and chemical or DNA-based, that are currently used for cheese geographical origin authentication, highlighting their principle, application, discriminative power, and advantages and limits, as well as to present future perspectives in this analytical field.

2. Chemical and Physical Methods for Cheese Origin Authentication

Polyphasic chemical and physical analysis approaches are now more common than single-parameter descriptions (i.e., dry matter, total protein, salt content) [36] to decipher cheese composition profiles. This is reinforced by the fact that cheese characterization, based on general chemical parameters, does not efficiently discriminate cheese geographical origin, as recently shown for water buffalo mozzarella by Salzano and colleagues [37]. That is why multiple signals are analyzed to acquire specific insight into typical cheese characteristics connected with its origin. Isotope and trace element fingerprinting methods have been considered as reference methods; however, other chemical and physical analyses can be used for geographical origin authentication.

2.1. Stable Isotope Ratio Mass Spectrometry

Stable isotope ratio mass spectrometry (IRMS) is among the most common methods for food geographical origin authentication [3]. It detects natural isotopic abundance of light and heavy stable isotopes, which mostly depend on climatic or geographical conditions (mainly latitude and altitude). The stable isotope ratio is also affected by biological and environmental interactions, and thus geographical product origins can be differentiated even if these have a high degree of similarity. Elements are called isotopes when their atoms are made by the same number of protons but a different number of neutrons, yielding a different atomic mass than the normal element [38]. Stable isotopes are non-radioactive isotopes and do not decay rapidly to form other elements.
Usually, stable isotope analysis is expressed as a ratio using an international standard to calculate it (Equation (1)):
δ‰ = (RSample − RStandard)/RStandard ∗ 1000
where R is the ratio between heavy and light isotopes [39]. The results of stable isotope ratios are always expressed as a percentage (‰—per mille unit) of international standard samples received from international organizations such as Vienna-Pee Dee Belemnite (V-PDB) for δ13C, Aria (AIR) for δ15N, Vienna—Standard Mean Ocean Water (V-SMOW) for δ2H and δ18O, and Vienna—Canyon DiabloTriolite (V-CDT) for δ34S [40].
IRMS has been widely used for cheese origin authentication [41,42,43] and many stable and unstable isotope ratios have been investigated. Among them, 13C/12C, 15N/14N, 2H/H, 18O/16O, and 34S/32S elements are the most commonly used, while 87Sr/86Sr, 44Ca/40Ca, 44Ca/42Ca, and 206Pb/204Pb are occasionally reported [38,44]. For cheese authentication, IRMS is based on predictable and reproducible responses of stable isotopes to typical factors such as geographical origin, animal origin, seasonality, and manufacturing processes [38]. Animal feed was shown to have the highest impact on δ13C and δ15N, while δ2H is heavily influenced by the animal diet, and its combination with δ18O is mainly impacted by geographical origin and seasonality [38,41]. δ34S is mostly linked to geographical origin, i.e., soil geology, and it is not correlated with other stable isotope ratios [45]. On the contrary, further studies are still needed to gauge the effect of cheese-making on stable isotope ratios [38] as different results on these ratios in milk and cheese have been reported. They suggested either no major difference between milk and cheese obtained after milk processing [41,43] or a partial impact [34,42]. Considering isotope abundances in different organic macromolecules, the casein fraction has been reported to be the most reliable for origin authentication [39].
In general, possible effects of the cheese-making process on stable isotope ratios could be related to fat removal (in particular, the glycerol fraction), curd acidification, curd clotting (e.g., use of a commercial starter vs. natural milk cultures), curd washing, curd heat treatment (e.g., 50 °C), and salt washing/brining, while ripening time has not yet been reported to impact their composition [38,46]. For the casein stable isotope ratio of milk, the corresponding cheese did not show any significant differences for δ13C, δ15N, and δ2H, but an unexplained and significant difference (p < 0.001) was reported for δ18O in typical pressed-cooked cheese [41]. The authors suggested a relevant fractionation in the animal in comparison with the feed, but a lack of isotopic fractionation during cheese-making [41]. However, Bontempo et al. [34] obtained a different isotopic ratio comparing milk and corresponding Mozzarella di Bufala Campana PDO for δ2H and δ18O. Further studies on milk and corresponding cheeses obtained through different processes and technologies may provide additional insight into the eventual changes of stable isotope ratios during cheese-making. Table 1 reports the advantages and limitations of stable isotope ratio mass spectrometry for cheese origin authentication.
From an applied point of view, IRMS was used to discriminate the origin of two typical mountain cheeses from Italy [41]. The authors combined δ13C, δ15N, δ2H, and δ18O from the casein fraction to build a canonical discriminant model that, after cross-validation, was able to correctly classify 96% of the milk and cheese samples. In Brazil, Silva and colleagues [42] analyzed δ13C, δ15N, δ2H, and δ18O from the water fraction of milk and cheese. After using the δ2H and δ18O in a linear regression model, they were able to discriminate milk and cheese samples according to their production area. In another study, Pillonel and colleagues [48] showed that the combined measurement of δ13C, δ15N, δ18O, and δ 34S/32S of the casein fraction authenticated Swiss vs. French Raclette cheeses.
In summary, isotope fingerprinting has proven to be reliable for the geographical origin authentication of typical cheeses. It is worth noting that stable isotope ratio analyses are already used as a traceability tool for some PDO cheeses such as Grana Padano and Parmigiano Reggiano (Regulation (EU) No. 1151/2012, amendment 2017/C 358/10 and 2018/C 132/07). This method is also often combined with trace element determination using inductively coupled plasma-mass spectrometry (ICP-MS) or inductively coupled plasma-atomic emission spectroscopy (ICP-AES).

2.2. Inductively Coupled Plasma

Cheese elemental fingerprinting is also currently used for origin authentication using inductively coupled plasma methods (ICP-MS or ICP-AES). These analyses rely on electromagnetic plasma to induce atom ionization (MS) or excitation (AES), to detect the different elements. Four main steps, including sample introduction and aerosol generation, plasma ionization/excitation, signal discrimination, and detection are used in ICP-MS and ICP-AES [44]. While MS analyzes ionized elements, AES detects light emitted by excited atoms. For cheese geographical origin authentication, ICP-MS is the preferred choice since it is a rapid, multi-element analysis able to quantify trace (ppm–ppb) or ultra-trace (ppb–ppq) elemental concentrations [49]. As elemental composition is mainly affected by geological and pedological traits, multiple factors can affect the element content in cheese, such as animal breed, feed vegetation, drinking water, and mineral supplementation [49,50]. Possible effects of cheese-making on cheese elements could derive from the clotting agent, curd acidification, manufacturing equipment, curd washing, and salt washing/brining [34,51]. Indeed, some authors reported that exclusion of Cu2+ and Zn2+ in multivariate analysis was necessary since a high transfer rate of these elements from dairy equipment to cheese was expected [52]. Table 2 reports the advantages and limitations of inductively coupled plasma for cheese origin authentication.
Elemental fingerprinting data obtained by Danezis and colleagues [50] have provided useful insights for an understanding of multiple element variations, including rare earth and precious metals, occurring in Greek Graviera cheeses obtained from 9 different regions (n = 105 samples). These authors analyzed 61 different elements, including rare earth (Dy, Er, Eu, Nd, Pr, Sc, Sm, Y, Yb), precious metals (Au, Pd, Re, Ru), ultra-trace elements (Nb, Ta, Tl, W, Zr), and trace elements (Ag, Al, Bi, Cd, Cu, Mo, Ni, Pb), and found significant differences according to cheese origin. They were able to correctly classify 92.1% of the tested cheeses (21 traditional and 9 commercial cheeses) using discriminant analysis [53]. The model was built on 65 elements, but the most significant variables were Ce, Dy, Eu, Gd, Ho, La, Nd, Pr, Sm, Tb, Y, Yb, Pd, As, Ba, Co, Fe, Ga, Mo, Ni, Ti, Zr, Ca, and P. Even if the authors did not perform any cross-validation, their results showed that ICP-MS was useful for cheese origin authentication, achieving high correct classification rates. According to the same study, rare earth elements seemed to significantly increase the discriminatory power of ICP-MS.
As previously stated, a combination of IRMS and ICP-based methods can also be used. For example, IRMS (for the casein fractions) and ICP-MS achieved high performance for cheese origin classifications. This was shown for semi-cooked typical Italian alpine cheeses, namely Asiago, Fontina, Toma, Vezzena Montasio, Spressa, and Puzzone (n = 109). The use of Ba, Ca, Cu, Ga, K, Mg, Rb, Zn, δ13Ccasein, δ15Ncasein, δ13Cglycerol, and δ18Oglycerol as predictive variables in canonical discriminative analysis was able to correctly classify 94% of the tested samples [52]. Another example was for authentication of PDO Parmigiano Reggiano vs. 11 imitation cheeses from different origins [54]. In their process, Camin et al. [54] used variables selected by the Random Forest algorithm (δ13Ccasein, δ2Hcasein, δ15Ncasein, δ34Scasein, and Sr, Cu, Mo, Re, Na, U, Bi, Ni, Fe, Mn, Ga, Se, and Li) to create a supervised two-class model that was able to correctly classify 98.3% of the 265 hard cheese samples. Lastly, Nečemer and colleagues [51] discriminated different Slovenian cheese origins by combining P, S, K, Cl, Ca, Zn, and δ13Ccasein and δ15Ncasein contents, and a high correct classification rate (97%) confirmed that the dual IRMS/ICP-MS approach provided robust data to authenticate cheese geographical origins.

2.3. Infrared Spectroscopy

Spectroscopic analyses, including near-infrared (NIR) and mid-infrared (MIR), are based on the selective interaction of infrared beams with food molecules [55]. The infrared region includes a wide energy range (800 nm–1 mm, 12,500–10 cm−1) and spectrophotometers can only evaluate a fraction of the wavelength, such as NIR (800–2500 nm, 12,500–4000 cm−1), MIR (2.5–25 µm, 4000–400 cm−1), and far-infrared (25–1000 µm, 400–10 cm−1) [56]. Although infrared spectroscopy mainly involves vibrational energy, NIR incorporates both electronic and vibrational spectroscopy, while MIR mainly monitors molecular vibrations and far-infrared contains rotatory and vibrational movements. NIR spectroscopy mainly reflects the absorption information of overtone and combination tone of chemical bond vibrations of hydrogen-containing groups (C–H, O–H, N–H, and S–H) that reflect the anharmonic constant and the high-frequency vibration of the fundamental stretching of a XH bond (i.e., second overtone transition of C–H and O–H in the 1050–1400 nm region) [55,56]. Bands in the NIR region are weak or very weak, making this region markedly different from the others, but at the same time, more difficult to analyze. However, compared to the MIR region, OH and NH stretching bands of monomeric and polymeric species are better separated and differentiate free terminal functional groups from those within the molecule [56]. On the other hand, MIR spectroscopy is a highly sensitive method in which polar functional groups such as C=O, OH, and C–S exhibit intense bands. These bands, combined with other specificities of this region, such as stronger bands from antisymmetric vs. symmetric stretching, make this analysis useful for molecular fingerprinting [56].
In food fingerprints, spectroscopic techniques have gained popularity since these are fast, solvent-free, automatic, non-destructive, non-invasive, inexpensive, and can be used as a multiparameter analysis [49]. In general, NIR spectroscopy (in reflectance or transmittance mode) is more often used than MIR spectroscopy for food analysis as it requires less sample preparation and can be easily used for in-field analysis [57].
Cheese has proven to be a challenging matrix for infrared spectroscopic analysis as it is non-homogeneous (e.g., crystalline structure, holes) and numerous cheese types exist [58]. Nevertheless, many studies on cheese characterized and correctly predicted the chemical composition, manufacturing technique, ripening time, seasonality, and feeding system of milk-producing animals [59,60,61,62]. On the other hand, only a limited number of studies have focused on cheese origin authentication, possibly connected to the initial inability to differentiate milk geographical origin using infrared spectroscopic techniques [63] and sample size needed to validate the analysis [61]. Table 3 reports the advantages and limitations of infrared spectroscopy for cheese origin authentication.
According to Niermöller and Holroyd [64], studies conducted on a reduced sample size (n < 60) reported weak calibration for NIR spectra since, according to the multivariate model employed for the chemometrics analysis, sample size should be higher; in fact, PLS may require hundreds of samples per category. In another study, NIR spectroscopy correctly classified 96% of cheese samples from pasture-fed and conserved-forage-fed (hay or grass silage) dairy cattle [66]. More recently, NIR was used (850 to 1048 nm wavelength region) for a cheese model obtained from different dairy systems, and 67.1% of samples were correctly classified after applying a cross-validation (LDA model) [62]; however, the aim was not to discriminate the geographical origin of the tested samples. In a study comparing MIR and NIR spectroscopy performances, Karoui et al. [67] correctly classified 86.6% and 85.7% of 91 Emmental PDO cheeses obtained from Switzerland, France, Finland, Germany, and Austria using factorial discriminant analysis, thus highlighting MIR and NIR performances to discriminate cheeses produced with a similar process but with different geographic origins. In another study, Karoui et al. [68] used two MIR regions (3000–2800 and 1500–900 cm−1) to successfully authenticate PDO Gruyère and L’Etivaz cheese, and 90.5% and 90.9% of samples were correctly classified using a factorial discriminant analysis.
In conclusion, although some studies have tested NIR and MIR spectroscopy to authenticate cheese origins, it is still difficult to conclude to what extent these techniques can be applied given the relatively low number of samples used in these studies [61,65]. In this context, further work on larger sample sizes for diverse cheese categories will likely improve the discriminatory power of these methods.

2.4. Nuclear Magnetic Resonance

Another spectroscopic analysis based on selective interactions between electromagnetic radiation and sample molecules is nuclear magnetic resonance (NMR). Generally, NMR uses radio frequency pulses to induce magnetic resonance nuclei-oriented transitions in an external magnetic field. When electromagnetic radiation hits nuclei, oriented nuclei move from lower to higher energy status or resonant nuclei. Subsequently, nuclei emit energy to return to the lower energy status, producing free induction decay [69]. The detected energy produces an absorbance signal, expressed in ppm, obtained from the ratio of standard molecules (e.g., 3-(trimethylsilyl)-propionate-d4). NMR, based on nuclei magnetic angular momentum—spin—is characterized by the azimuthal quantum number (I). Only nuclei with an even number of neutrons and an odd number of protons can be detected by their magnetic angular momentum (e.g., 12C and 16O have I = 0, while 1H and 13C have I > 1). Different NMR methods have already been employed in food authentication. However, considering cheese geographical origin authentication, 1H high-resolution magic angle spinning (HRMAS) NMR is the most commonly used technique [70,71]. For example, Shintu and Caldarelli [71] applied a discriminant analysis using unsaturated fatty acid, aspartic acid, serine, and olefinic proton signals to classify the geographical origin of 20 Emmental cheeses from Austria, Finland, France, Germany, and Switzerland. They correctly classified 89.5% of samples after cross-validation. Mazzei and Piccolo [70] successfully identified Mozzarella di Bufala Campana PDO cheese origin. The studied cheeses were obtained in the same region but from two different provinces, namely Salerno and Caserta. These authors applied a discriminant analysis model based on four metabolites linked to milk processing (β-galactose, β-lactose, acetic acid, and glycerol) and, after cross-validation, 100% of samples were correctly classified. Similarly, Consonni and Cagliani [72] correctly differentiated 93.5% of Italian Parmigiano Reggiano PDO cheeses from foreign eastern European “Grana type” cheeses by applying a PLS-DA model on leucine, isoleucine, lactate, butanoate, and acetate. The same authors also discriminated Parmigiano Reggiano PDO cheeses based on their ripening times. However, no cross-validation was performed to test the developed model.
The simultaneous analysis of proteins, lipids, and other metabolite fractions by 1H HRMAS NMR offers great opportunities for cheese geographical origin authentications [73]. Moreover, NMR offers multiple advantages, such as simple sample preparation, multiple metabolite quantifications, high experimental reproducibility, and it is also non-destructive. Nevertheless, some limits should be considered, such as high cost for acquisition and maintenance, and higher limits of detection (typically, 10 to 100 times) when compared to gas chromatography-mass spectrometry [73,74]. Table 4 reports the advantages and limitations of nuclear magnetic resonance for cheese origin authentication.

2.5. Gas Chromatography-Based Fatty Acid Analysis

Recently, fatty acid analysis has been investigated for cheese origin authentication purposes. Fatty acids are lipid components formed by carboxylic acids with saturated or unsaturated aliphatic carbon chains [75]. Generally, fatty acid analysis is based on four sequential steps: extraction, derivatization, chromatographic separation, and detection [75]. The analytical reference method for fatty acid analysis is gas chromatography (GC), which is usually combined with flame ionization detectors [76]. For GC, analytes are vaporized in a heated chamber and transported by high-pressure inert gas (e.g., N2, He) through the stationary phase (i.e., column material), where selective interaction leads to compound separation [77]. Subsequently, based on their retention index, compounds are eluted into the hydrogen flame of the detector, creating an electrical signal [78].
Fatty acid analysis to authenticate milk origin has already been reported [79]. These authors were able to efficiently authenticate the geographical origin of milk as the combination of different feeding strategies, herd and farm management practices (leading to distinct feed fatty acid profiles), grazing, breeding, animals’ genetics, animals’ rumen microbiota, and difference in lactation days varied considerably according to geographic locations. In traditional cheese, milk fatty acid profiles are also impacted by manufacturing practices (e.g., use of Cynara cardunculus L. as a coagulating agent), cheese microbiota (see the section on DNA-based methods for cheese origin authentication), and ripening times [80,81]. For example, fatty acid profiles were used and correctly authenticated the producer origins of Serra da Estrela PDO cheeses from Portugal, even within a limited production area [76]. These authors used 12 fatty acids, namely caproic, caprylic, undecanoic, lauric, pentadecanoic, palmitic, palmitoleic, heptadecanoic, oleic, linoleic trans-isomer, heneicosanoic, and arachidonic acids, in a linear discriminant model to achieve an 88% correct classification rate after cross-validation. Higher classification rates (95% after cross-validation) were also reported by Margalho et al. [82] using 13 fatty acids for 11 artisanal cheeses produced in 5 major geographical regions in Brazil (sample size n = 402). Similarly, high classification percentages were also achieved by Danezis et al. [83], who analyzed 101 PDO and 11 non-PDO cheeses from Greece. These authors used pH, moisture, fat, NaCl, and linoleic acid contents to correctly discriminate the PDO from non-PDO cheeses (excluding similar hard PDO cheese) and achieved 100% correct classifications. Although high correct classification rates were obtained by Margalho et al. [82] and Danezis et al. [83] for cheese origin authentication, the analyzed cheeses greatly differed in terms of physical aspect (i.e., spread, soft, semi-hard, and hard), fat content, and ripening time. In recent studies dealing with origin authentication of similar traditional cheeses, the correct classification rates reported by Reis Lima et al. [76], Gatzias et al. [84], and Vatavali et al. [85] were, respectively, 88%, 88.2%, and 91.1%.

2.6. Gas Chromatography-Based Volatilome Analysis

Among the different strategies to authenticate cheese origin, volatile organic compound (VOC) analysis has gained attention as volatile compounds, which result from cheese microbiota metabolic activities, are an important component of cheese typicity. Indeed, through glycolysis, proteolysis, and lipolysis, cheese microbiota produce a wide range of VOCs. These include aldehydes, ketones, alcohols, esters, lactones, hydrocarbons, free fatty acids (n < 10 carbon atoms), sulfur compounds, and amines that provide the typical cheese aroma [86,87,88]. In this context, GC-MS is most commonly used to analyze cheese volatilomes (see Medina et al. [89] for technical details on cheese VOC analysis using GC-MS).
Using volatile fingerprinting, Pillonel et al. [90] discriminated country of origin for both PDO and non-PDO Emmental cheeses based on butan-2-one, 3-hydroxybutanone, butan-2-ol, and octene concentrations by principal component analysis (PCA). The concentrations of 21 other volatile compounds also showed at least one significant difference connected with their origin. Cheese origin discrimination relied on the fact that volatile profiles varied both qualitatively and quantitatively according to their country or region of origin [90]. For example, PDO Emmental from Switzerland was differentiated from Polish and French Emmental cheeses based on free-fatty acid qualitative composition (2-methyl butanoic acid for PDO Emmental cheese compared with 3-methylbutanoic acid in French and Polish Emmental ones) and relative abundance (such as nonanoic acid), alcohol presence/absence (3 methylbut-2-en-1-ol presence only in Swiss Emmental), as well as other aliphatic hydrocarbons, ketones, aldehydes, and esters. More recently, Pluta-Kubica et al. [91] also differentiated Emmental cheese origin based on their VOC profiles.
Similarly, Salzano et al. [37] used GC-MS to authenticate water buffalo mozzarella PDO cheese from the non-PDO versions. Both milk and cheese samples were analyzed, and differences were highlighted for both matrices using partial least squares discriminant analysis (PLS-DA). Variable importance in projection (VIP) analysis selected the 15 highest scored variables. Among them, talopyranose, 2,3-dihydroxypropyl icosanoate, sorbose, 4-phenyl glutamic acid, oxalic acid, and galactose were the most prevalent in typical PDO mozzarella, while tagatose, lactic acid dimer, ribitol, dodecyl thioglycolate, n-acetyl glucosamine, valine, and diethylene glycol were more abundant in non-PDO mozzarella. These authors concluded that the combination of multiple practices, such as forage from the same region, natural milk starters with both LAB and yeast instead of citric acid, and different packaging, all impacted the volatilome of the final product. These differences could thus explain what distinguished the water buffalo mozzarella produced according to PDO rules vs. those not following such guidelines (i.e., non-PDO mozzarella).
Another study authenticated Pecorino cheese origins, namely Pecorino Romano PDO, Pecorino Sardo PDO, and Pecorino di Farindola (certified by the Slow Food Foundation) [92]. The authors compared VOC fingerprints by a linear discriminant analysis (LDA) and PLS-DA model. The most influential variables in the LDA model were 2-methyl butyl isovalerate, butan-2-one, butyl butanoate, ethyl acetate, nonan-2-one, and propan-2-one, while in the PLS-DA model, VIP analysis identified 14 relevant compounds, namely, butan-2-one, pentan-2-ol, ethyl acetate, dicaprylyl ether, propanoic acid, 3-methylbutan-1-ol, propan-2-ol, ethyl decanoate, heptan-2-ol, butan-2-ol, butyl butanoate, pentan-2-one, ethanol, and 2-methylpropanoic acid. Only six compounds were common to the two tested models. Considering that both models yielded similar classification performances (total classification rate of 87.5% after cross-validation), differences in the most influential variables linked to origin authentication were related to the index score value applied in the VIP analysis. Indeed, while the authors used an index score of 1, a previous study by Salzano et al. [37] used a higher value (i.e., 1.5) to select variables of interest and obtain a high correct classification rate for water buffalo mozzarella. Noteworthy, Vatavali et al. [85] only classified 47.5% of Graviera cheese origins, and thus the discriminative power of VOC profiles for cheese authentication could vary considerably according to the considered cheese type.
GC-MS fingerprints can also be exploited to identify biomarkers connected with specific attributes of traditional products, such as animal feed requirements. Indeed, Caligiani et al. [93] validated a method to quantify cyclopropane fatty acids (e.g., dihydrosterculic acid) as biomarkers for cows fed with corn silage. PDO cheeses such as Parmigiano Reggiano, Fontina, Comté, and Gruyère do not permit silage to be used in cow feed, and thus the absence of cyclopropane fatty acids in such cheeses may confirm correct feeding management.
Another feed-associated fraction of cheese VOCs are terpenoids. Terpenoids are a highly diversified class of naturally occurring organic compounds or phytochemicals, also called isoprenoids. These are derived from isoprene units and produced by dicotyledon plants [88]. In Slovenian cheeses, VOC analysis discriminated cheeses into 4 clusters (average silhouette 0.764) according to their geographical origin and based on 9 monoterpenes, namely, α-pinene, camphene, α-phellandrene, β-pinene, 3-carene, 2-carene, limonene, tricyclene, and γ-terpinene [94]. In a similar way, Turri et al. [88] identified significant differences in 10 terpenes between pasture-producer of Historic Rebel cheese, and the results suggested that allo-ocimene, α-terpinolene, α-pinene, and δ-3-carene could be possible biomarkers to differentiate cheese origin. Overall, these studies highlighted that volatilome analysis can be an interesting tool for cheese origin authentication, although classification rate performances can vary among cheese varieties.
Another approach to directly analyze volatile compounds is the electronic nose (e-nose). This analytical technology, designed to mimic the human olfactory system [95], has gained interest in food authentication as it is highly correlated with consumer perception [96]. A typical e-nose comprises the sampling system, a set of non-selective sensors or mass spectrometer (MS), and a pattern-recognition system [97,98]. Nowadays, different sensors are used, such as metal-oxide semiconductors, conducting polymers, and piezoelectric crystal sensors [99]. In the case of cheese origin authentication, only a limited number of studies reported an e-nose strategy. For example, Pillonel et al. [90] obtained similar classification rates using a PCA model to evaluate Emmental cheese origins (90%). In the case of Pecorino cheeses, an e-nose and artificial neural network approach correctly classified 96.5% of Pecorino di Fossa PDO cheeses (n = 18) and Pecorino cheeses of other origins (n = 48) [100]. In conclusion, while different authors have reported e-noses to authenticate foods subjected to different frauds, including geographical origin [98,101,102], only a limited number of studies concerned cheese origin authentication. Table 5 reports the advantages and limitations of VOC analysis for cheese origin authentication.
Some important attributes that may impact the efficiency of this approach to discriminate cheese origin are provided. One important attribute to differentiate cheese is ripening time [106]. Typical cheeses are sold according to a minimum ripening time. However, according to the initial extent of ripening, significant changes may occur during shelf-life. Contamination during portioning, inadequate temperature usage during transport, light exposure, and storage conditions may impact VOC profiles. In this context, specific VOC fractions, such as terpenoids, combined with VOC and microbiota correlations, may overcome possible changes in VOC profiles at the retailing stage. However, to our best knowledge, no studies have evaluated the effect of retail on cheese origin authentication.

3. DNA-Based Methods for Cheese Origin Authentication

As previously mentioned, the metabolic activities of cheese microbiota play a crucial role in the development of cheese typicity. For geographical origin authentication, microbiota fingerprinting is therefore of high interest as traditional and artisanal cheeses are produced with a more diversified microbiota associated with the cheese-making process (e.g., use of raw milk, starter, brine, equipment and materials, and ripening rooms). Distinct differences in the composition of this complex microbiota, composed of Gram-positive and -negative bacteria, fungi, archaea, and viruses, could be used for cheese origin authentication.
The first study on cheese microbiota using high-throughput sequencing (HTS) was performed by Quigley et al. [107] on traditional cheeses. Since then, numerous studies have been published on many aspects linked to cheese quality and typicity. While cheese microbial diversity was traditionally investigated using culture-dependent methods, hence overlooking unculturable or subdominant species, nowadays, culture-independent methods (HTS) have unraveled this diversity and provided further means to connect microbiota composition to cheese quality and typicity, but also origin. This success is due to the availability of new sequencing platforms, bioinformatic pipelines, and a continuous decrease in cost. Among high-throughput sequencing, amplicon sequencing—or selective amplification of polymorphic genes across their hypervariable regions—is the most widely reported in the scientific literature [108]. In this context, the use of DNA metabarcoding (also known as metagenetics) to study cheese microbiota was proposed as a tool for cheese origin authentication [33].
To perform DNA metabarcoding, cheese samples are first homogenized, then total DNA is most frequently extracted using commercial kits, ad hoc protocols, or a combination thereof [109,110,111]. Hypervariable regions of taxonomically relevant genes (e.g., 16S rDNA for bacteria and archaea, ITS, 18S rDNA, 26S rDNA for fungi) are amplified by PCR reactions, while a second amplification step tags amplicons with specific DNA fragments—barcodes—and dedicated adapters for the final sequencing step using next-generation technologies (e.g., Illumina, Pacbio, Iontorrent, or Nanopore). For further details on sampling, library preparation, and sequencing platforms, the reviews by Hugerth et al. [112] and Tilocca et al. [113] are suggested.
Typically, 16S rDNA and ITS (internal transcribed spacer) markers, targeting bacteria and fungi, respectively, are employed to generate compositional data describing microbial taxa and their relative abundance in cheese microbial communities. After sequencing, two complementary but different ways can be used for amplicon clustering from quality-checked data, namely, operational taxonomic units (OTUs) and amplicon sequencing variants (ASVs) [114]. On one hand, pipelines such as QIIME and IMNGS build sequence clusters based on their similarity (usually using a similarity cutoff of 97%) to obtain OTUs [115,116]. On the other hand, ASVs are obtained with the DADA2 pipeline (also available in QIIME2) by inferring biological sequences in a sample, and discerning sequence variant differences down to a single nucleotide [117,118]. Subsequently, taxonomic assignment is performed using a specific classifier tool (BLAST, RDP, UCLUST, SortMeRNA) against various reference databases, such as Greengenes, SILVA, and UNITE [119,120]. Generally, clustered OTUs/ASVs are analyzed from the phylum to the genus level since they can be less precise at the species level [121]. Identified taxa can be divided into dominant, subdominant, and rare sequences, representing 40% to 90%, 1% to 0.01%, or 0.01% to 0.0001% of reads per sample, respectively [122].
Using these DNA methods to determine cheese microbiota composition, literature data have shown that the core is usually dominated by lactic acid bacteria (LAB) [123], including starter and non-starter lactic acid bacteria (NSLAB), while in the case of cheese with edible rinds, rinds are usually dominated by salt-tolerant fungi and bacteria from the Actinomycetota, Bacillota, and Pseudomonadota phyla. Cheese microorganisms can be deliberately inoculated into the milk or on the cheese surface as starter or secondary cultures, but may also originate from multiple reservoirs, including raw milk (i.e., for raw milk cheeses), brine or salt, or dairy environments (cheese-making equipment and ripening shelves). A distinctive case corresponds to raw milk cheeses as cheese microbiota can be differentiated according to the amount of starter cultures employed during cheese-making and the origin of the raw milk used, at the farm level [124]. Since milk quality depends on many factors (e.g., animal health status, breed, lactation stage, teat skin, hides, feces management, farm dimension, feeding system, season, farm staff hygiene, and management), recent longitudinal studies have connected some of the main characteristics with farm origin [125,126,127]. Indeed, for raw milk cheeses, cheese microbiota can be directly impacted by raw milk microbiota [107], thus, both microbiota can be used for cheese origin authentication. Generally, most protected land- and tradition-related labeled cheeses are produced from raw milk, and the complex microbiota encountered in raw milk directly influences the unique cheese sensorial properties appreciated by consumers. As an example, in the EU, among the 284 cheeses recognized for their typicity, over 180 are raw milk cheeses ([128] accessed on 3 October 2022). Besides raw milk, traditional tools/equipment and the dairy environment are also shaping cheese microbiota at both species and strain levels. In fact, some key species may originate from the dairy environment [129]. In addition, as reported by Bokulich and Mills [130] and Calasso et al. [131] for LAB, different strains of a given species can colonize the dairy environment, and thus, the cheese. More recently, Sun et al. [132] determined that in-house microbiota were essential in shaping Bethlehem (PA, USA), a Saint-Nectaire-type cheese produced without starters by traditional methods. Using 16S rRNA amplicon sequencing and SourceTracker (a bioinformatic tool based on Bayesian inference that estimates the proportion of different sources contributing to a designated microbial community) [133], these authors identified wooden vats—used for overnight ripening—as a major source of desirable LAB that shaped cheese microbiota from acidification to ripening. Similar findings were reported by Montel et al. [134], who highlighted that traditional cheeses have complex and rich microbiota influenced by traditional equipment. Indeed, traditional cheeses are produced using cheese-making practices that tend to increase microbial diversity via contact with diverse microorganisms originating from dairy equipment [135]. Moreover, differences in cheese-making technologies (e.g., use of natural milk or whey culture rather than commercial starters, use of rennet or clotting agents, curd cooking, draining process, salting) and farm practices (e.g., type of housing, silage, grassland) between different production areas can also shape the cheese microbial community [136]. Considering these factors, we can question whether traditional cheeses can be differentiated based on their origin and if the main factors that affect cheese microbiota complexity and diversity play a significant role.

3.1. Main Factors Affecting Cheese Microbial Diversity

In this context, in a recent study by Kamimura et al. [137], the microbiota of 578 traditional Brazilian cheeses were analyzed with amplicon sequencing. Bacterial communities were distinctly clustered with PCA by cheese type and regional origins while, at the genus level, hierarchical cluster analysis separated production regions. These authors were thus able to identify specific origin-related microbiota. The core microbiota of Brazilian traditional cheeses displayed different relative abundances and oligotypes (i.e., closely related but distinct bacterial taxa) of LAB belonging to the Enterococcus, Lactococcus, Streptococcus, Leuconostoc, and Lactobacillus sensu lato genera, as well as other taxa belonging to the Enterobacteriaceae family and Staphylococcus genus. Within the same regional area, microbiota analysis differentiated the origin—Cerrado, Araxà, Canastra, Campos des Vertenes, and Serro—of traditional cheeses produced with a similar natural whey starter and ripening period (17 and 22 days). These findings were in agreement with those of another study that analyzed 97 samples of Minas artisanal cheese from 6 different producers located in the same region [138]. Starter cultures, consisting of Streptococcus, Lactococcus, and Lactobacillus sensu lato spp., constituted the core microbiota in all farms. However, significant differences in family- and genus-level bacterial community relative abundances were observed between the studied farms due to environmental factors such as geographical location. Even when dominant genera may be inferred to the natural whey cultures used, meta-analysis from amplicon sequencing data of traditional artisanal cheeses from Italy, Belgium, and Kalmykia indicated differences in bacterial structures between cheeses produced in different geographic areas (unweighted PCoA cluster MANOVA, p < 0.001 [139]). Those produced using natural milk cultures showed improved acidification without an effect on the typical cheese microbiota. Indeed, at the end of the ripening period, cheese origins clustered according to producer facilities (PCoA on Bray–Curtis) [140].
Another study was performed by Zago et al. [136]. In this case, 118 Grana Padano samples were analyzed after 7–8 months of ripening and a common core microbiota composed of Lactobacillus-, Lactococcus-, Lacticaseibacillus-, Limosilactobacillus-, and Streptococcus-dominant genera was observed. More precisely, differences in bacterial abundance, richness, and evenness were found for dominant and sub-dominant groups according to production region, a result also confirmed by PERMANOVA beta-diversity analysis. The authors also identified specific species that could be linked to several production areas; however, no species biomarkers were identified regardless of production area and non-metric multidimensional scaling did not show any clear clustering profile.
Some cheeses are produced in very small geographical zones by a limited number of producers. This is the case for Plaisentif and Historic Rebel cheeses from the mountainous regions in Italy, that are only produced during specific seasons (violet blooming season and grazing season) by 14 and 12 producers, respectively [88,122]. Both are raw milk cheeses produced without starter adjunction. Bacterial amplicon sequencing analysis (16S rDNA V4 region) for Plaisentif cheese identified dominant genera and, more importantly, differences in bacterial community profiles between producers thus detected fraudulent starter additions in some cheeses [122].
Based on a similar analysis for Historical Rebel cheese, the core microbiota was composed of 5 different genera—Streptococcus, Lactobacillus, Lactococcus, Leuconostoc, and Pediococcus—with Streptococcus relative abundances ranging from 60% to 85% [88]. Richness and other alpha-diversity parameters differed among producers as well as in multivariate analysis (PCoA on unweighted Unifrac), and based on the observed significant differences, pasture area could be linked to the different Historic Rebel cheese producers.

3.2. Climatic and Environmental Condition

Another factor that can impact microbial communities of traditional cheeses are the climatic and dairy environment conditions that are directly associated with geographical origins. This was observed for traditional Chinese Rushan cheese produced using Chaenomeles sinensis boiled extract as a clotting agent in three different regions. Even if the same UHT milk and production equipment were used, geographical origins significantly impacted the relative abundance of 12 dominant genera, namely Lactobacillus, Acinetobacter, Acetobacter, Lactococcus, Enterobacter, Moraxella, Enterococcus, Streptococcus, Kocuria, Staphylococcus, Chryseobacterium, and Exiguobacterium [141], and is likely related to specific house microbiota and open-air drying. This result was also confirmed by PCoA clusters and Anosim analysis.
As typical cheese microbiota can be in part acquired from the specific raw materials used, traditional tools, environmental and production conditions, cheese-making process, and geographical area, a comparison between traditional and industrial cheeses may provide additional information to authenticate cheese origin. Noteworthy, some authors have reported that commercial starters, inoculated at ~106 CFU/mL, prevent resident microbiota from developing, especially during ripening [124,142]. Overall, milk pasteurization, use of similar commercial starters, similar industrial equipment, and standardized recipes for cheese production are crucial factors that decrease cheese microbial complexity and biodiversity and lead to highly standardized productions. These directly deplete the unicity of the matrix, and thus lower variance is detected (Figure 1). This hypothesis is in accordance with the study by Kamilari et al. [143], in which a significant decrease in bacterial diversity was observed in industrially produced Haloumi cheeses vs. artisanal products. However, the microbial diversity observed for artisanal Haloumi cheeses could not link them to their producers’ geographical origins. In another study, aiming to authenticate cheese origin at the producer level, no distinction could again be made [144].

3.3. Cheese Ripening

Cheese ripening is another factor that affects microbial community diversity and cheese typicity. Ripening can be considered as a selection process that leads to cheese microbial composition changes. According to Gobetti et al. [123], intentionally added microorganisms used in cheese-making include primary starters (natural milk culture, natural whey culture, or lyophilized commercial starters), secondary or adjunct LAB starters, and milk autochthonous microbiota (NSLAB and others). These are the main ripening agents in intermediate to long ripening times, which mainly explain the observed diversity and typicity of the produced cheeses. The relationship between primary starters and NSLAB during maturation is well-known and involves a progressive reduction of the former in favor of the latter. The role of NSLAB is crucial in maturation and the development of the typical characteristics of traditional cheese. In this case, useful insights can be gained by comparing the genomic features of primary starters for the presence of genes involved in the metabolic pathways important for cheese maturation. While primary starters have important genetic features for the utilization of lactose—mainly connected with their acidification ability—NSLAB possess many more genes coding for peptidases, peptide transporters within cells, and amino acid catabolism, that can represent an advantage during cheese maturation [145]. Moreover, compared to primary starters, NSLAB tend to adapt better to the hostile conditions of the cheese ripening environment, such as temperature, salt content, pH, and redox potential. In fact, NSLAB can adopt alternative metabolic pathways to produce energy from unconventional sources while resisting acid conditions. Therefore, NSLAB present in raw milk at a sufficient inoculum to colonize ripened raw milk cheeses, or acquired from the house microbiota, could be an indicator of geographical origin at the producer level. Beyond NSLAB, other microorganisms belonging to various groups can influence cheese ripening. This is the case of fungal communities in many traditional cheeses, such as Queijo de Azeitão in Portugal [146], Tomme d’Orchies in France [110], and Robiola di Roccaverano in Italy [147]. Indeed, fungal communities are well-known for their decisive role in flavor and texture of white and blue-veined mold-ripened cheeses due to lipolytic, proteolytic, and glycolytic activities, leading to high production of aromatic ketones and alcohols [134,148,149]. Generally, fungal species such as Penicillium camemberti, Penicillium roqueforti, Debaryomyces hansenii, Kluyveromyces marxianus, Candida catenulata, Galactomyces geotrichum, and Mucor lanceolatus are either deliberately added as technological adjunct cultures or present in the production environment [149,150,151,152]. Nevertheless, in traditional cheeses, fungal communities were reported to be more diverse than the used starter but, at the same time, not connected with geographical origin [109,146]. Table 6 reports advantages and limitations of amplicon sequencing for cheese origin authentication.
The mentioned studies showed that in traditional cheeses, the combination of artisanal cheese-making, specific raw materials, and characteristic environmental conditions shape microbial community diversity according to geographical origin. Most analyses conducted using 16S rDNA amplicon sequencing discriminated cheese origin, although taxonomic classification was still limited to genus/family-level descriptions and only a few cheese types per study were considered. To further assess the unicity of typical cheeses against food fraud, more in-depth studies, including meta-analyses on all available cheese data and increased depth of microbial population descriptions (e.g., metagenomics), are of interest.

4. Conclusions and Perspectives

Geographical origin authentication is an important safeguard for food quality and safety but also from an economical point of view as it enables consumer protection and provides technical support to enforce national and international legislations [49]. Applying elemental and isotopic characterization, volatilome or microbiota analysis to typical products can protect them from food fraud and improve registration processes and marketing decision-making [161]. Nevertheless, our knowledge on authentication methods is far from complete. Indeed, most studies and methodologies employed to authenticate cheese origins only provide qualitative answers (e.g., does the method discriminate origin?) and often lack quantitative assessment (to what extent can different cheese origins be discriminated from each other?). This is probably connected to the complexity of commonly used multivariate models such as PLS, PLS-DA, and LDA, and the use of specific algorithms (e.g., Random Forest, VIP) for variable selection and the need for internal or interlaboratory external and cross-validations.
Chemometrics approaches differ among analytical technologies. In this case, infrared methods—especially NIR spectroscopy—have consistently been applied together with chemometrics analysis to achieve good classification rates after cross-validation, but the number of samples employed for authentication purposes was often limited. Stable isotope ratios combined with trace element analysis have been shown to be the most accurate methods to authenticate cheese origin, since available studies reported consistent statistical analysis and modeling with high correct classification rates after cross-validation. Nevertheless, the actual discriminative power of the method for closely distant cheese producers remains unknown. Moreover, the impact of animal feeding and the high cost per sample must be considered. Considering that many production disciplinaries declare a minimum amount of local forage in animal feed, one possible strategy to increase cheese typicity and improve authentication using stable isotopic and elemental metabolomics would be to increase the use of local feed or use grazing.
Regarding DNA-based methods, amplicon sequencing can discriminate cheese geographical origin. However, some important considerations to assess traditional cheese origin are related to metabarcoding approach limitations. Indeed, microbial species and strains that originate from the dairy environment and that characterize traditional product origins could be key biomarkers for cheese authentication. In this context, shotgun metagenomics offers multiple advantages in comparison with amplicon metabarcoding, since no amplification step is required and all the genetic material in the sample is used for genome reconstruction to reach a deeper taxonomic assignation, potentially to the strain level [162,163]. For example, StrainPhlAn uses unique gene family markers and sample-specific consensus sequences to infer strain-level genotypes from different environments [164]. Considering that cheese is formed by microorganisms of different domains, appropriate sequencing depths would directly provide insight on bacteria, fungi, and viruses in the same analysis. These tools could thus be applied to milk, starters, and typical cheeses to obtain accurate DNA-based fingerprints to efficiently authenticate product origin [164]. This approach may help explore new traceability systems based on crucial components of fermented products, such as their virome [165]. Moreover, shotgun metagenomic analyses of gene richness, as a possible indicator of microbial community adaptation to different stress conditions, would be useful to reconstruct metabolic pathways connected with specific cheese traits, such as volatile compound production and metabolites that characterize typical cheeses. In this sense, integrated systems biology, combining metabolomics and metagenomics, could improve our knowledge on this subject, as only a few studies to date have combined both techniques [88,156,166]. While microbiota-based studies have compared typical and non-typical products, some omics approaches reported differences between typical and industrial products, thus making it difficult to clearly determine the factors that characterize typical food products. Artificial intelligence approaches, such as deep learning and machine learning, should be taken into consideration to improve classification rates and better-differentiate authentic and fraudulent products [167]. Overall, further research focused on comparing how well DNA-based analyses perform in comparison to the actual reference analyses (i.e., isotope fingerprinting and trace element analysis) used to authenticate cheese origin is needed. A combined approach, using isotope fingerprinting or trace element analysis and metagenomics, to obtain the highest discriminative power for cheese geographical origin authentication could also be of interest.

Author Contributions

M.C. (Marco Cardin), E.C. and J.M. conceptualized the layout of the review; M.C. (Marco Cardin) wrote the original draft, created the associated Tables and Figures, and edited the manuscript; B.C. contributed to the manuscript on cheese fraud and methodological aspects; E.N. contributed to physical and chemical aspects, and M.C. (Monika Coton) to volatilome aspects; J.M. contributed to DNA-based methods, and E.C. supervised the work and contributed to all aspects, including writing—review and editing; B.C., E.N. and E.C. obtained the funding. All authors have read and agreed to the published version of the manuscript.


This work was completed in the framework of Marco Cardin’s Ph.D. grant funded by the Università degli Studi di Padova (Italy) and the Université de Bretagne Occidentale (France). Funding number 20192413451/ISCR_D1.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Robson, K.; Dean, M.; Haughey, S.; Elliott, C. A Comprehensive Review of Food Fraud Terminologies and Food Fraud Mitigation Guides. Food Control 2021, 120, 107516. [Google Scholar] [CrossRef]
  2. Regulation (EU) No. 625/2017 of the European Parliament and of the Council of 15 March 2017 on Official Controls and Other Official Activities Performed to Ensure the Application of Food and Feed Law, Rules on Animal Health and Welfare, Plant Health and Plant Protection Products, 95, 1–95. Available online: (accessed on 3 October 2022).
  3. Eurofins Analytics France. FoodIntegrity Handbook: A Guide to Food Authenticity Issues and Analytical Solutions, 1st ed.; Morin, J.-F., Lees, M., Eds.; Eurofins Analytics France: Nantes, France, 2018. [Google Scholar] [CrossRef]
  4. Spink, J.W. The Current State of Food Fraud Prevention: Overview and Requirements to Address ‘How to Start?’ And ‘How Much Is Enough?’. Curr. Opin. Food Sci. 2019, 27, 130–138. [Google Scholar] [CrossRef]
  5. Rocchi, B.; Romano, D.; Sadiddin, A.; Stefani, G. Assessing the economy-wide impact of food fraud: A SAM-based counterfactual approach. Agribusiness 2020, 36, 167–191. [Google Scholar] [CrossRef][Green Version]
  6. Moore, J.C.; Spink, J.; Lipp, M. Development and Application of a Database of Food Ingredient Fraud and Economically Motivated Adulteration from 1980 to 2010. J. Food Sci. 2012, 77, R118–R126. [Google Scholar] [CrossRef]
  7. Everstine, K.; Spink, J.; Kennedy, S. Economically Motivated Adulteration (EMA) of Food: Common Characteristics of EMA Incidents. J. Food Prot. 2013, 76, 723–735. [Google Scholar] [CrossRef] [PubMed]
  8. Hong, E.; Lee, S.Y.; Jeong, J.Y.; Park, J.M.; Kim, B.H.; Kwon, K.; Chun, H.S. Modern Analytical Methods for the Detection of Food Fraud and Adulteration by Food Category: Adulterated Food Categories and Their Analytical Methods. J. Sci. Food Agric. 2017, 97, 3877–3896. [Google Scholar] [CrossRef] [PubMed]
  9. Breitenbach, R.; Rodrigues, H.; Brandão, J.B. Whose Fault Is It? Fraud Scandal in the Milk Industry and Its Impact on Product Image and Consumption—The Case of Brazil. Food Res. Int. 2018, 108, 475–481. [Google Scholar] [CrossRef]
  10. Europol-Interpol Operation Results in Global Seizures of Fake and Illicit Food—OPSON VII [Media Release] 2019. Available online: (accessed on 2 October 2022).
  11. Europol-Interpol Operation Results in Global Seizures of Fake and Illicit Food—OPSON IX [Media Release] 2021. Available online: (accessed on 2 October 2022).
  12. Gimonkar, S.; E. Van Fleet, E.; Boys, K.A. Dairy Product Fraud. In Food Fraud; Elsevier: Rockville, MD, USA, 2021; pp. 249–279. [Google Scholar] [CrossRef]
  13. Montgomery, H.; Haughey, S.A.; Elliott, C.T. Recent Food Safety and Fraud Issues within the Dairy Supply Chain (2015–2019). Glob. Food Secur. 2020, 26, 100447. [Google Scholar] [CrossRef]
  14. European Commission. The EU Food Fraud Network and the System for Administrative Assistance—Food Fraud. 2018. Available online: (accessed on 2 October 2022).
  15. BRCGS, British Retail Consortium Global Standards Global Food Safety Standard (Issue 8) British Retail Consortium Global Standards. 2018. Available online: (accessed on 2 October 2022).
  16. FSSC 22000, Foundation Food Safety System Certification 22000 FSSC 22000 Scheme Version 5. Available online: (accessed on 2 October 2022).
  17. GFSI, Global Food Safety Initiative Tackling Food Fraud through Food Safety Management Systems. GFSI 2018. Available online: (accessed on 2 October 2022).
  18. European Committee for Standardization with the CEN Workshop Agreement (CEN WS/86) (Work Item Number WS086002)—Authenticity and Fraud in the Feed and Food Chain—Concepts, Terms, and Definitions (Reference CWA 17369:2019). Available online: (accessed on 2 October 2022).
  19. Codex Alimentarius International Food Standard. General Standard for Cheese. 1978. Available online: (accessed on 2 October 2022).
  20. Valletta, M.; Ragucci, S.; Landi, N.; Di Maro, A.; Pedone, P.V.; Russo, R.; Chambery, A. Mass Spectrometry-Based Protein and Peptide Profiling for Food Frauds, Traceability and Authenticity Assessment. Food Chem. 2021, 365, 130456. [Google Scholar] [CrossRef]
  21. Vandecandelaere, E.; Arfini, F.; Belletti, G.; Marescotti, A.; Allaire, G.; Cadilhon, J.; Casabianca, F.; Damary, P.H.G.; Estève, M.; Hilmi, M.; et al. Uniendo Personas, Territorios y Productos. Guía para Fomentar la Calidad Vinculada al Origen y las Indicaciones Geográficas Sostenibles. FAO 2010. Available online: (accessed on 2 October 2022).
  22. Picon, A. Cheese Microbial Ecology and Safety. In Global Cheesemaking Technology; Papademas, P., Bintsis, T., Eds.; John Wiley & Sons, Ltd: Chichester, UK, 2017; pp. 71–99. [Google Scholar] [CrossRef]
  23. Regulation (EU) No. 1151/2012 of the European Parliament and of the Council of 21 November 2012 on Quality Schemes for Agricultural Products and Foodstuffs; Official Journal of the European Union: Brussels, European Union; Volume 343, pp. 1–29. Available online: (accessed on 3 October 2022).
  24. Regulation (EU) No. 665/2014 of the European Parliament and of the Council of 11 March 2014 on Supplementing Regulation (EU) No 1151/2012 of the European Parliament and of the Council with Regard to Conditions of Use of the Optional Quality Term ‘Mountain Product’; Official Journal of the European Union: Brussels, European Union; Volume 179, pp. 23–25. Available online: (accessed on 3 October 2022).
  25. European Commission. Joint Research Centre. Institute for Prospective Technological Studies. EU Island Farming and the Labelling of Its Products; Publications Office: Luxembourg, European Union, 2013. [Google Scholar]
  26. Sidali, K.L.; Capitello, R.; Manurung, A.J.T. Development and Validation of the Perceived Authenticity Scale for Cheese Specialties with Protected Designation of Origin. Foods 2021, 10, 248. [Google Scholar] [CrossRef] [PubMed]
  27. Regulation (EU) No. 178/2002 of the European Parliament and of the Council of 28 January 2002 Laying down the General Principles and Requirements of Food Law, Establishing the European Food Safety Authority and Laying down Procedures in Matters of Food Safety; Official Journal of the European Communities: Brussels, European Union, 1 February 2002. Available online: (accessed on 3 October 2022).
  28. Regulation (EU) No. 1169/2011 of the European Parliament and of the Council of 25 October 2011 on the Provision of Food Information to Consumers, Amending Regulations (EC) No 1924/2006 and (EC) No 1925/2006 of the European Parliament and of the Council, and repealing Commission Directive 87/250/EEC, Council Directive 90/496/EEC, Commission Directive 1999/10/EC, Directive 2000/13/EC of the European Parliament and of the Council, Commission Directives 2002/67/EC and 2008/5/EC and Commission Regulation (EC) No 608/2004 Text with EEA relevance; Official Journal of the European Communities: Brussels, European Union; Volume 020. Available online: (accessed on 3 October 2022).
  29. Decreto del Ministero delle Politiche Agricole Alimentari e Forestali del 9 dicembre 2016 Indicazione Dell’origine in Etichetta della Materia Prima per il latte e i Prodotti Lattieri Caseari, in Attuazione del Regolamento (UE) n. 1169/2011, Relativo alla Fornitura di Informazioni sugli Alimenti ai Consumatori. GU Repubblica Italiana n. 15 del 19.01.2017. Available online: (accessed on 2 October 2022).
  30. Real Decreto 1181/2018, de 21 de Septiembre, Relativo a la Indicación del origen de la leche Utilizada como Ingrediente en el Etiquetado de la Leche y los Productos Lácteos. Available online: (accessed on 4 October 2022).
  31. WTO. Uruguay Round Agreement: TRIPS. In Part II—Standards Concerning the Availability, Scope and Use of Intellectual Property Rights, Section 3: Geographical Indications; World Trade Organization: Geneva, Switzerland, 1994; Available online: (accessed on 4 October 2022).
  32. Esteki, M.; Shahsavari, Z.; Simal-Gandara, J. Gas Chromatographic Fingerprinting Coupled to Chemometrics for Food Authentication. Food Rev. Int. 2020, 36, 384–427. [Google Scholar] [CrossRef]
  33. Kamilari, E.; Tomazou, M.; Antoniades, A.; Tsaltas, D. High Throughput Sequencing Technologies as a New Toolbox for Deep Analysis, Characterization and Potentially Authentication of Protection Designation of Origin Cheeses? Int. J. Food Sci. 2019, 2019, 1–15. [Google Scholar] [CrossRef] [PubMed][Green Version]
  34. Bontempo, L.; Barbero, A.; Bertoldi, D.; Camin, F.; Larcher, R.; Perini, M.; Sepulcri, A.; Zicarelli, L.; Piasentier, E. Isotopic and Elemental Profiles of Mediterranean Buffalo Milk and Cheese and Authentication of Mozzarella Di Bufala Campana PDO: An Initial Exploratory Study. Food Chem. 2019, 285, 316–323. [Google Scholar] [CrossRef] [PubMed]
  35. Ruth, S.M.v.; Granato, D. Food Identity, Authenticity and Fraud: The Full Spectrum. Foods 2017, 6, 49. [Google Scholar] [CrossRef][Green Version]
  36. Kędzierska-Matysek, M.; Florek, M.; Skałecki, P.; Litwińczuk, A.; Chruścicki, A. A comparison of the physicochemical characteristics of the regional cheese Oscypek and the traditional cheese Gazdowski from the Polish Podhale. Int. J. Dairy Technol. 2014, 67, 283–289. [Google Scholar] [CrossRef]
  37. Salzano, A.; Manganiello, G.; Neglia, G.; Vinale, F.; De Nicola, D.; D’Occhio, M.; Campanile, G. A Preliminary Study on Metabolome Profiles of Buffalo Milk and Corresponding Mozzarella Cheese: Safeguarding the Authenticity and Traceability of Protected Status Buffalo Dairy Products. Molecules 2020, 25, 304. [Google Scholar] [CrossRef][Green Version]
  38. O’Sullivan, R.; Schmidt, O.; Monahan, F.J. Stable Isotope Ratio Analysis for the Authentication of Milk and Dairy Ingredients: A Review. Int. Dairy J. 2022, 126, 105268. [Google Scholar] [CrossRef]
  39. Camin, F.; Bertoldi, D.; Santato, A.; Bontempo, L.; Perini, M.; Ziller, L.; Stroppa, A.; Larcher, R. Validation of Methods for H, C, N and S Stable Isotopes and Elemental Analysis of Cheese: Results of an International Collaborative Study: Validation of Methods for Isotopic and Elemental Analysis of Cheese. Rapid Commun. Mass Spectrom. 2015, 29, 415–423. [Google Scholar] [CrossRef]
  40. Novelli, E.; Balzan, S.; Perini, M.; Antonetti, P.; Fasolato, L.; Camin, F. ANALYTICAL EMPLOYMENT OF STABLE ISOTOPES OF CARBON, NITROGEN, OXYGEN AND HYDROGEN FOR FOOD AUTHENTICATION. Ital. J. Food Saf. 2011, 1, 71. [Google Scholar] [CrossRef]
  41. Bontempo, L.; Lombardi, G.; Paoletti, R.; Ziller, L.; Camin, F. H, C, N and O Stable Isotope Characteristics of Alpine Forage, Milk and Cheese. Int. Dairy J. 2012, 23, 99–104. [Google Scholar] [CrossRef][Green Version]
  42. Silva, A.V.; Hélie, J.F.; Caxito, F.d.A.; Monardes, H.; Mustafa, A.F.; Stevenson, R. Multi-Stable Isotope Analysis as a Tool for Assessing the Geographic Provenance of Dairy Products: A Case Study Using Buffalo’s Milk and Cheese Samples from the Amazon Basin, Brazil. Int. Dairy J. 2014, 35, 107–110. [Google Scholar] [CrossRef]
  43. Altieri, S.; Saiano, K.; Biondi, M.; Ricci, P.; Lubritto, C. Traceability of ‘Mozzarella Di Bufala Campana’ Production Chain by Means of Carbon, Nitrogen and Oxygen Stable Isotope Ratios. J. Sci. Food Agric. 2020, 100, 995–1003. [Google Scholar] [CrossRef]
  44. Luykx, D.M.A.M.; van Ruth, S.M. An Overview of Analytical Methods for Determining the Geographical Origin of Food Products. Food Chem. 2008, 107, 897–911. [Google Scholar] [CrossRef]
  45. Pianezze, S.; Bontempo, L.; Perini, M.; Tonon, A.; Ziller, L.; Franceschi, P.; Camin, F. δ34S for tracing the origin of cheese and detecting its authenticity. J. Mass Spectrom. 2020, 55, e4451. [Google Scholar] [CrossRef] [PubMed]
  46. Manca, G.; Camin, F.; Coloru, G.C.; Del Caro, A.; Depentori, D.; Franco, M.A.; Versini, G. Characterization of the Geographical Origin of Pecorino Sardo Cheese by Casein Stable Isotope (13C/12C and 15N/14N) Ratios and Free Amino Acid Ratios. J. Agric. Food Chem. 2001, 49, 1404–1409. [Google Scholar] [CrossRef]
  47. Katerinopoulou, K.; Kontogeorgos, A.; Salmas, C.E.; Patakas, A.; Ladavos, A. Geographical Origin Authentication of Agri-Food Products: A Review. Foods 2020, 9, 489. [Google Scholar] [CrossRef]
  48. Pillonel, L.; Tabacchi, R.; Bosset, J.O. Geographic authenticity of Swiss Cheeses: Selected results for the food control laboratories and perspective for the future. Mitt. Lebensm. Hyg. 2004, 95, 503–513. [Google Scholar]
  49. Danezis, G.P.; Tsagkaris, A.S.; Camin, F.; Brusic, V.; Georgiou, C.A. Food Authentication: Techniques, Trends & Emerging Approaches. TrAC Trends Anal. Chem. 2016, 85, 123–132. [Google Scholar] [CrossRef]
  50. Danezis, G.; Theodorou, C.; Massouras, T.; Zoidis, E.; Hadjigeorgiou, I.; Georgiou, C. Greek Graviera Cheese Assessment through Elemental Metabolomics—Implications for Authentication, Safety and Nutrition. Molecules 2019, 24, 670. [Google Scholar] [CrossRef][Green Version]
  51. Nečemer, M.; Potočnik, D.; Ogrinc, N. Discrimination between Slovenian cow, goat and sheep milk and cheese according to geographical origin using a combination of elemental content and stable isotope data. J. Food Compos. Anal. 2016, 52, 16–23. [Google Scholar] [CrossRef]
  52. Bontempo, L.; Larcher, R.; Camin, F.; Hölzl, S.; Rossmann, A.; Horn, P.; Nicolini, G. Elemental and Isotopic Characterisation of Typical Italian Alpine Cheeses. Int. Dairy J. 2011, 21, 441–446. [Google Scholar] [CrossRef]
  53. Danezis, G.P.; Pappas, A.C.; Tsiplakou, E.; Pappa, E.C.; Zacharioudaki, M.; Tsagkaris, A.S.; Papachristidis, C.A.; Sotirakoglou, K.; Zervas, G.; Georgiou, C.A. Authentication of Greek Protected Designation of Origin Cheeses through Elemental Metabolomics. Int. Dairy J. 2020, 104, 104599. [Google Scholar] [CrossRef]
  54. Camin, F.; Wehrens, R.; Bertoldi, D.; Bontempo, L.; Ziller, L.; Perini, M.; Nicolini, G.; Nocetti, M.; Larcher, R. H, C, N and S Stable Isotopes and Mineral Profiles to Objectively Guarantee the Authenticity of Grated Hard Cheeses. Anal. Chim. Acta 2012, 711, 54–59. [Google Scholar] [CrossRef]
  55. Yang, J.; Xu, J.; Zhang, X.; Wu, C.; Lin, T.; Ying, Y. Deep Learning for Vibrational Spectral Analysis: Recent Progress and a Practical Guide. Anal. Chim. Acta 2019, 1081, 6–17. [Google Scholar] [CrossRef] [PubMed]
  56. Ozaki, Y. Infrared Spectroscopy—Mid-Infrared, Near-Infrared, and Far-Infrared/Terahertz Spectroscopy. Anal. Sci. 2021, 37, 1193–1212. [Google Scholar] [CrossRef]
  57. De Marchi, M.; Penasa, M.; Zidi, A.; Manuelian, C.L. Invited Review: Use of Infrared Technologies for the Assessment of Dairy Products—Applications and Perspectives. J. Dairy Sci. 2018, 101, 10589–10604. [Google Scholar] [CrossRef][Green Version]
  58. Holroyd, S.E. The Use of near Infrared Spectroscopy on Milk and Milk Products. J. Near Infrared Spectrosc. 2013, 21, 311–322. [Google Scholar] [CrossRef]
  59. Lerma-García, M.J.; Gori, A.; Cerretani, L.; Simó-Alfonso, E.F.; Caboni, M.F. Classification of Pecorino Cheeses Produced in Italy According to Their Ripening Time and Manufacturing Technique Using Fourier Transform Infrared Spectroscopy. J. Dairy Sci. 2010, 93, 4490–4496. [Google Scholar] [CrossRef]
  60. Ottavian, M.; Facco, P.; Barolo, M.; Berzaghi, P.; Segato, S.; Novelli, E.; Balzan, S. Near-Infrared Spectroscopy to Assist Authentication and Labeling of Asiago d’allevo Cheese. J. Food Eng. 2012, 113, 289–298. [Google Scholar] [CrossRef]
  61. Stocco, G.; Cipolat-Gotet, C.; Ferragina, A.; Berzaghi, P.; Bittante, G. Accuracy and Biases in Predicting the Chemical and Physical Traits of Many Types of Cheeses Using Different Visible and Near-Infrared Spectroscopic Techniques and Spectrum Intervals. J. Dairy Sci. 2019, 102, 9622–9638. [Google Scholar] [CrossRef]
  62. Bergamaschi, M.; Cipolat-Gotet, C.; Cecchinato, A.; Schiavon, S.; Bittante, G. Chemometric Authentication of Farming Systems of Origin of Food (Milk and Ripened Cheese) Using Infrared Spectra, Fatty Acid Profiles, Flavor Fingerprints, and Sensory Descriptions. Food Chem. 2020, 305, 125480. [Google Scholar] [CrossRef] [PubMed]
  63. Coppa, M.; Martin, B.; Agabriel, C.; Chassaing, C.; Sibra, C.; Constant, I.; Graulet, B.; Andueza, D. Authentication of Cow Feeding and Geographic Origin on Milk Using Visible and Near-Infrared Spectroscopy. J. Dairy Sci. 2012, 95, 5544–5551. [Google Scholar] [CrossRef] [PubMed][Green Version]
  64. Niemöller, A.; Holroyd, S. Applications of near infrared spectrometry for the analysis of milk and milk products. Bull. Int. Dairy Fed. 2019, 497, 19. [Google Scholar]
  65. Bittante, G.; Patel, N.; Cecchinato, A.; Berzaghi, P. Invited Review: A Comprehensive Review of Visible and near-Infrared Spectroscopy for Predicting the Chemical Composition of Cheese. J. Dairy Sci. 2022, 105, 1817–1836. [Google Scholar] [CrossRef]
  66. Andueza, D.; Agabriel, C.; Constant, I.; Lucas, A.; Martin, B. Using Visible or near Infrared Spectroscopy (NIRS) on Cheese to Authenticate Cow Feeding Regimes. Food Chem. 2013, 141, 209–214. [Google Scholar] [CrossRef]
  67. Karoui, R.; Dufour, É.; Pillonel, L.; Schaller, E.; Picque, D.; Cattenoz, T.; Bosset, J.-O. The Potential of Combined Infrared and Fluorescence Spectroscopies as a Method of Determination of the Geographic Origin of Emmental Cheeses. Int. Dairy J. 2005, 15, 287–298. [Google Scholar] [CrossRef]
  68. Karoui, R.; Mazerolles, G.; Bosset, J.; Debaerdemaeker, J.; Dufour, E. Utilisation of Mid-Infrared Spectroscopy for Determination of the Geographic Origin of Gruyère PDO and L’Etivaz PDO Swiss Cheeses. Food Chem. 2007, 105, 847–854. [Google Scholar] [CrossRef]
  69. Mazzei, P.; Piccolo, A. HRMAS NMR Spectroscopy Applications in Agriculture. Chem. Biol. Technol. Agric. 2017, 4, 11. [Google Scholar] [CrossRef][Green Version]
  70. Mazzei, P.; Piccolo, A. 1H HRMAS-NMR Metabolomic to Assess Quality and Traceability of Mozzarella Cheese from Campania Buffalo Milk. Food Chem. 2012, 132, 1620–1627. [Google Scholar] [CrossRef]
  71. Shintu, L.; Caldarelli, S. Toward the Determination of the Geographical Origin of Emmental(Er) Cheese via High Resolution MAS NMR: A Preliminary Investigation. J. Agric. Food Chem. 2006, 54, 4148–4154. [Google Scholar] [CrossRef]
  72. Consonni, R.; Cagliani, L.R. Ripening and geographical characterization of Parmigiano Reggiano cheese by 1H NMR spectroscopy. Talanta 2008, 76, 200–205. [Google Scholar] [CrossRef] [PubMed]
  73. Balthazar, C.F.; Guimarães, J.T.; Rocha, R.S.; Pimentel, T.C.; Neto, R.P.C.; Tavares, M.I.B.; Graça, J.S.; Alves Filho, E.G.; Freitas, M.Q.; Esmerino, E.A.; et al. Nuclear Magnetic Resonance as an Analytical Tool for Monitoring the Quality and Authenticity of Dairy Foods. Trends Food Sci. Technol. 2021, 108, 84–91. [Google Scholar] [CrossRef]
  74. Emwas, A.-H.; Roy, R.; McKay, R.T.; Tenori, L.; Saccenti, E.; Gowda, G.A.N.; Raftery, D.; Alahmari, F.; Jaremko, L.; Jaremko, M.; et al. NMR Spectroscopy for Metabolomics Research. Metabolites 2019, 9, 123. [Google Scholar] [CrossRef] [PubMed][Green Version]
  75. Hewavitharana, G.G.; Perera, D.N.; Navaratne, S.B.; Wickramasinghe, I. Extraction Methods of Fat from Food Samples and Preparation of Fatty Acid Methyl Esters for Gas Chromatography: A Review. Arab. J. Chem. 2020, 13, 6865–6875. [Google Scholar] [CrossRef]
  76. Reis Lima, M.J.; Bahri, H.; Sá Morais, J.; Veloso, A.C.A.; Fontes, L.; Lemos, E.T.; Peres, A.M. Assessing Serra Da Estrela PDO Cheeses’ Origin-Production Date Using Fatty Acids Profiles. Food Meas. 2019, 13, 2988–2997. [Google Scholar] [CrossRef][Green Version]
  77. Ahad, T.; Nissar, J. Fingerprinting in Determining the Adultration of Food. J. Pharmacogn. Phytochem. 2017, 6, 1543–1553. [Google Scholar]
  78. Holm, T. Aspects of the Mechanism of the Flame Ionization Detector. J. Chromatogr. A 1999, 842, 221–227. [Google Scholar] [CrossRef]
  79. Gaspardo, B.; Lavrenčič, A.; Levart, A.; Del Zotto, S.; Stefanon, B. Use of milk fatty acids composition to discriminate area of origin of bulk milk. J. Dairy Sci. 2010, 93, 3417–3426. [Google Scholar] [CrossRef][Green Version]
  80. Bonanno, A.; Tornambè, G.; Bellina, V.; De Pasquale, C.; Mazza, F.; Maniaci, G.; Di Grigoli, A. Effect of farming system and cheesemaking technology on the physicochemical characteristics, fatty acid profile, and sensory properties of Caciocavallo Palermitano cheese. J. Dairy Sci. 2013, 96, 710–724. [Google Scholar] [CrossRef][Green Version]
  81. Fox, P.F.; Guinee, T.P.; Cogan, T.M.; McSweeney, P.L.H. Microbiology of Cheese Ripening. In Fundamentals of Cheese Science; Springer: Boston, MA, USA, 2017; pp. 333–390. [Google Scholar] [CrossRef]
  82. Margalho, L.P.; Kamimura, B.A.; Pimentel, T.C.; Balthazar, C.F.; Araujo, J.V.A.; Silva, R.; Conte-Junior, C.A.; Raices, R.S.L.; Cruz, A.G.; Sant’Ana, A.S. A Large Survey of the Fatty Acid Profile and Gross Composition of Brazilian Artisanal Cheeses. J. Food Compos. Anal. 2021, 101, 103955. [Google Scholar] [CrossRef]
  83. Danezis, G.P.; Tsiplakou, E.; Pappa, E.C.; Pappas, A.C.; Mavrommatis, A.; Sotirakoglou, K.; Georgiou, C.A.; Zervas, G. Fatty Acid Profile and Physicochemical Properties of Greek Protected Designation of Origin Cheeses, Implications for Authentication. Eur. Food Res. Technol. 2020, 246, 1741–1753. [Google Scholar] [CrossRef]
  84. Gatzias, I.S.; Karabagias, I.K.; Kontominas, M.G.; Badeka, A.V. Geographical Differentiation of Feta Cheese from Northern Greece Based on Physicochemical Parameters, Volatile Compounds and Fatty Acids. LWT 2020, 131, 109615. [Google Scholar] [CrossRef]
  85. Vatavali, K.; Kosma, I.; Louppis, A.; Gatzias, I.; Badeka, A.V.; Kontominas, M.G. Characterisation and Differentiation of Geographical Origin of Graviera Cheeses Produced in Greece Based on Physico-Chemical, Chromatographic and Spectroscopic Analyses, in Combination with Chemometrics. Int. Dairy J. 2020, 110, 104799. [Google Scholar] [CrossRef]
  86. Bhandari, M.P.; Carmona, E.N.; Galstyan, V.; Sberveglieri, V. Quality Evaluation of Parmigiano Reggiano Cheese by a Novel Nanowire Device S3 and Evaluation of the VOCs Profile. Procedia Eng. 2016, 168, 460–464. [Google Scholar] [CrossRef]
  87. Afshari, R.; Pillidge, C.J.; Dias, D.A.; Osborn, A.M.; Gill, H. Cheesomics: The Future Pathway to Understanding Cheese Flavour and Quality. Crit. Rev. Food Sci. Nutr. 2020, 60, 33–47. [Google Scholar] [CrossRef] [PubMed]
  88. Turri, F.; Cremonesi, P.; Battelli, G.; Severgnini, M.; Brasca, M.; Gandini, G.; Pizzi, F. High Biodiversity in a Limited Mountain Area Revealed in the Traditional Production of Historic Rebel Cheese by an Integrated Microbiota–Lipidomic Approach. Sci. Rep. 2021, 11, 10374. [Google Scholar] [CrossRef] [PubMed]
  89. Medina, S.; Perestrelo, R.; Silva, P.; Pereira, J.A.M.; Câmara, J.S. Current Trends and Recent Advances on Food Authenticity Technologies and Chemometric Approaches. Trends Food Sci. Technol. 2019, 85, 163–176. [Google Scholar] [CrossRef]
  90. Pillonel, L.; Ampuero, S.; Tabacchi, R.; Bosset, J. Analytical Methods for the Determination of the Geographic Origin of Emmental Cheese: Volatile Compounds by GC/MS-FID and Electronic Nose. Eur. Food Res. Technol. 2003, 216, 179–183. [Google Scholar] [CrossRef]
  91. Pluta-Kubica, A.; Domagała, J.; Gąsior, R.; Wojtycza, K.; Witczak, M. Characterisation of the Profile of Volatiles of Polish Emmental Cheese. Int. Dairy J. 2021, 116, 104954. [Google Scholar] [CrossRef]
  92. Di Donato, F.; Biancolillo, A.; Mazzulli, D.; Rossi, L.; D’Archivio, A.A. HS-SPME/GC–MS Volatile Fraction Determination and Chemometrics for the Discrimination of Typical Italian Pecorino Cheeses. Microchem. J. 2021, 165, 106133. [Google Scholar] [CrossRef]
  93. Caligiani, A.; Nocetti, M.; Lolli, V.; Marseglia, A.; Palla, G. Development of a Quantitative GC–MS Method for the Detection of Cyclopropane Fatty Acids in Cheese as New Molecular Markers for Parmigiano Reggiano Authentication. J. Agric. Food Chem. 2016, 64, 4158–4164. [Google Scholar] [CrossRef] [PubMed]
  94. Tompa, G.; Susi, R.; Rogelj, I.; Pompe, M. Cryotrap/SPME/GC/MS Method for Profiling of Monoterpenes in Cheese and Their Clustering According to Geographic Origin. Acta Chim. Slov. 2013, 60, 595–603. [Google Scholar] [PubMed]
  95. Song, S.; Yuan, L.; Zhang, X.; Hayat, K.; Chen, H.; Liu, F.; Xiao, Z.; Niu, Y. Rapid Measuring and Modelling Flavour Quality Changes of Oxidised Chicken Fat by Electronic Nose Profiles through the Partial Least Squares Regression Analysis. Food Chem. 2013, 141, 4278–4288. [Google Scholar] [CrossRef] [PubMed]
  96. Fujioka, K. Comparison of Cheese Aroma Intensity Measured Using an Electronic Nose (E-Nose) Non-Destructively with the Aroma Intensity Scores of a Sensory Evaluation: A Pilot Study. Sensors 2021, 21, 8368. [Google Scholar] [CrossRef] [PubMed]
  97. Gardner, J.W.; Bartlett, P.N. A Brief History of Electronic Noses. Sens. Actuators B Chem. 1994, 18, 210–211. [Google Scholar] [CrossRef]
  98. Gliszczyńska-Świgło, A.; Chmielewski, J. Electronic nose as a tool for monitoring the authenticity of food. A Rev. Food Anal. Methods 2017, 10, 1800–1816. [Google Scholar] [CrossRef][Green Version]
  99. Ampuero, S.; Bosset, J.O. The Electronic Nose Applied to Dairy Products: A Review. Sens. Actuators B Chem. 2003, 94, 1–12. [Google Scholar] [CrossRef]
  100. Cevoli, C.; Cerretani, L.; Gori, A.; Caboni, M.F.; Gallina Toschi, T.; Fabbri, A. Classification of Pecorino Cheeses Using Electronic Nose Combined with Artificial Neural Network and Comparison with GC–MS Analysis of Volatile Compounds. Food Chem. 2011, 129, 1315–1319. [Google Scholar] [CrossRef]
  101. Lytou, A.E.; Panagou, E.Z.; Nychas, G.-J.E. Volatilomics for Food Quality and Authentication. Curr. Opin. Food Sci. 2019, 28, 88–95. [Google Scholar] [CrossRef]
  102. Roy, M.; Yadav, B.K. Electronic Nose for Detection of Food Adulteration: A Review. J. Food Sci. Technol. 2022, 59, 846–858. [Google Scholar] [CrossRef]
  103. Gómez-Ruiz, J.Á.; Ballesteros, C.; Viñas, M.Á.G.; Cabezas, L.; Martínez-Castro, I. Relationships between Volatile Compounds and Odour in Manchego Cheese: Comparison between Artisanal and Industrial Cheeses at Different Ripening Times. Lait 2002, 82, 613–628. [Google Scholar] [CrossRef][Green Version]
  104. Ardö, Y.; McSweeney, P.L.; Magboul, A.A.; Upadhyay, V.K.; Fox, P.F. Biochemistry of cheese ripening: Proteolysis. In Cheese; Academic Press: Cambridge, MA, USA, 2017; pp. 445–482. [Google Scholar] [CrossRef]
  105. Galle, S.A.; Koot, A.; Soukoulis, C.; Cappellin, L.; Biasioli, F.; Alewijn, M.; van Ruth, S.M. Typicality and Geographical Origin Markers of Protected Origin Cheese from The Netherlands Revealed by PTR-MS. J. Agric. Food Chem. 2011, 59, 2554–2563. [Google Scholar] [CrossRef] [PubMed]
  106. D’Incecco, P.; Limbo, S.; Hogenboom, J.; Rosi, V.; Gobbi, S.; Pellegrino, L. Impact of extending hard-cheese ripening: A multiparameter characterization of Parmigiano reggiano cheese ripened up to 50 months. Foods 2020, 9, 268. [Google Scholar] [CrossRef] [PubMed][Green Version]
  107. Quigley, L.; O’Sullivan, O.; Beresford, T.P.; Ross, R.P.; Fitzgerald, G.F.; Cotter, P.D. High-Throughput Sequencing for Detection of Subpopulations of Bacteria Not Previously Associated with Artisanal Cheeses. Appl. Environ. Microbiol. 2012, 78, 5717–5723. [Google Scholar] [CrossRef][Green Version]
  108. Kleerebezem, R.; Stouten, G.; Koehorst, J.; Langenhoff, A.; Schaap, P.; Smidt, H. Experimental Infrastructure Requirements for Quantitative Research on Microbial Communities. Curr. Opin. Biotechnol. 2021, 67, 158–165. [Google Scholar] [CrossRef] [PubMed]
  109. Wolfe, B.E.; Button, J.E.; Santarelli, M.; Dutton, R.J. Cheese Rind Communities Provide Tractable Systems for In Situ and In Vitro Studies of Microbial Diversity. Cell 2014, 158, 422–433. [Google Scholar] [CrossRef][Green Version]
  110. Ceugniez, A.; Taminiau, B.; Coucheney, F.; Jacques, P.; Delcenserie, V.; Daube, G.; Drider, D. Fungal Diversity of “Tomme d’Orchies” Cheese during the Ripening Process as Revealed by a Metagenomic Study. Int. J. Food Microbiol. 2017, 258, 89–93. [Google Scholar] [CrossRef][Green Version]
  111. Duru, I.C.; Laine, P.; Andreevskaya, M.; Paulin, L.; Kananen, S.; Tynkkynen, S.; Auvinen, P.; Smolander, O.-P. Metagenomic and Metatranscriptomic Analysis of the Microbial Community in Swiss-Type Maasdam Cheese during Ripening. Int. J. Food Microbiol. 2018, 281, 10–22. [Google Scholar] [CrossRef]
  112. Hugerth, L.W.; Andersson, A.F. Analysing Microbial Community Composition through Amplicon Sequencing: From Sampling to Hypothesis Testing. Front. Microbiol. 2017, 8, 1561. [Google Scholar] [CrossRef][Green Version]
  113. Tilocca, B.; Costanzo, N.; Morittu, V.M.; Spina, A.A.; Soggiu, A.; Britti, D.; Roncada, P.; Piras, C. Milk Microbiota: Characterization Methods and Role in Cheese Production. J. Proteom. 2020, 210, 103534. [Google Scholar] [CrossRef]
  114. Reitmeier, S.; Hitch, T.C.A.; Treichel, N.; Fikas, N.; Hausmann, B.; Ramer-Tait, A.E.; Neuhaus, K.; Berry, D.; Haller, D.; Lagkouvardos, I.; et al. Handling of Spurious Sequences Affects the Outcome of High-Throughput 16S RRNA Gene Amplicon Profiling. ISME Commun. 2021, 1, 31. [Google Scholar] [CrossRef]
  115. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Peña, A.G.; Goodrich, J.K.; Gordon, J.I.; et al. QIIME Allows Analysis of High-Throughput Community Sequencing Data. Nat. Methods 2010, 7, 335–336. [Google Scholar] [CrossRef] [PubMed][Green Version]
  116. Lagkouvardos, I.; Joseph, D.; Kapfhammer, M.; Giritli, S.; Horn, M.; Haller, D.; Clavel, T. IMNGS: A Comprehensive Open Resource of Processed 16S RRNA Microbial Profiles for Ecology and Diversity Studies. Sci. Rep. 2016, 6, 33721. [Google Scholar] [CrossRef] [PubMed][Green Version]
  117. Callahan, B.J.; McMurdie, P.J.; Rosen, M.J.; Han, A.W.; Johnson, A.J.A.; Holmes, S.P. DADA2: High-Resolution Sample Inference from Illumina Amplicon Data. Nat. Methods 2016, 13, 581–583. [Google Scholar] [CrossRef][Green Version]
  118. Bolyen, E.; Rideout, J.R.; Dillon, M.R.; Bokulich, N.A.; Abnet, C.C.; Al-Ghalith, G.A.; Alexander, H.; Alm, E.J.; Arumugam, M.; Asnicar, F.; et al. Reproducible, Interactive, Scalable and Extensible Microbiome Data Science Using QIIME 2. Nat. Biotechnol. 2019, 37, 852–857. [Google Scholar] [CrossRef]
  119. Quast, C.; Pruesse, E.; Yilmaz, P.; Gerken, J.; Schweer, T.; Yarza, P.; Peplies, J.; Glöckner, F.O. The SILVA Ribosomal RNA Gene Database Project: Improved Data Processing and Web-Based Tools. Nucleic Acids Res. 2012, 41, D590–D596. [Google Scholar] [CrossRef]
  120. Nilsson, R.H.; Larsson, K.-H.; Taylor, A.F.S.; Bengtsson-Palme, J.; Jeppesen, T.S.; Schigel, D.; Kennedy, P.; Picard, K.; Glöckner, F.O.; Tedersoo, L.; et al. The UNITE Database for Molecular Identification of Fungi: Handling Dark Taxa and Parallel Taxonomic Classifications. Nucleic Acids Res. 2019, 47, D259–D264. [Google Scholar] [CrossRef]
  121. Mizrahi-Man, O.; Davenport, E.R.; Gilad, Y. Taxonomic Classification of Bacterial 16S RRNA Genes Using Short Sequencing Reads: Evaluation of Effective Study Designs. PLoS ONE 2013, 8, e53608. [Google Scholar] [CrossRef]
  122. Dalmasso, A.; Soto del Rio, M.d.l.D.; Civera, T.; Pattono, D.; Cardazzo, B.; Bottero, M.T. Characterization of Microbiota in Plaisentif Cheese by High-Throughput Sequencing. LWT-Food Sci. Technol. 2016, 69, 490–496. [Google Scholar] [CrossRef]
  123. Gobbetti, M.; Di Cagno, R.; Calasso, M.; Neviani, E.; Fox, P.F.; De Angelis, M. Drivers That Establish and Assembly the Lactic Acid Bacteria Biota in Cheeses. Trends Food Sci. Technol. 2018, 78, 244–254. [Google Scholar] [CrossRef]
  124. Piqueras, J.; Chassard, C.; Callon, C.; Rifa, E.; Theil, S.; Lebecque, A.; Delbès, C. Lactic Starter Dose Shapes S. Aureus and STEC O26:H11 Growth, and Bacterial Community Patterns in Raw Milk Uncooked Pressed Cheeses. Microorganisms 2021, 9, 1081. [Google Scholar] [CrossRef] [PubMed]
  125. Porcellato, D.; Smistad, M.; Bombelli, A.; Abdelghani, A.; Jørgensen, H.J.; Skeie, S.B. Longitudinal Study of the Bulk Tank Milk Microbiota Reveals Major Temporal Shifts in Composition. Front. Microbiol. 2021, 12, 616429. [Google Scholar] [CrossRef] [PubMed]
  126. Skeie, S.B.; Håland, M.; Thorsen, I.M.; Narvhus, J.; Porcellato, D. Bulk Tank Raw Milk Microbiota Differs within and between Farms: A Moving Goalpost Challenging Quality Control. J. Dairy Sci. 2019, 102, 1959–1971. [Google Scholar] [CrossRef][Green Version]
  127. Guo, X.; Yu, Z.; Zhao, F.; Sun, Z.; Kwok, L.-Y.; Li, S. Both Sampling Seasonality and Geographic Origin Contribute Significantly to Variations in Raw Milk Microbiota, but Sampling Seasonality Is the More Determining Factor. J. Dairy Sci. 2021, 104, 10609–10627. [Google Scholar] [CrossRef] [PubMed]
  128. Licitra, G.; Caccamo, M.; Lortal, S. Artisanal Products Made With Raw Milk. In Raw Milk; Elsevier: San Diego, CA, USA, 2019; pp. 175–221. [Google Scholar] [CrossRef]
  129. Mounier, J.; Gelsomino, R.; Goerges, S.; Vancanneyt, M.; Vandemeulebroecke, K.; Hoste, B.; Scherer, S.; Swings, J.; Fitzgerald, G.F.; Cogan, T.M. Surface Microflora of Four Smear-Ripened Cheeses. Appl. Environ. Microbiol. 2005, 71, 6489–6500. [Google Scholar] [CrossRef][Green Version]
  130. Bokulich, N.A.; Mills, D.A. Facility-Specific “House” Microbiome Drives Microbial Landscapes of Artisan Cheesemaking Plants. Appl. Environ. Microbiol. 2013, 79, 5214–5223. [Google Scholar] [CrossRef][Green Version]
  131. Calasso, M.; Ercolini, D.; Mancini, L.; Stellato, G.; Minervini, F.; Di Cagno, R.; De Angelis, M.; Gobbetti, M. Relationships among House, Rind and Core Microbiotas during Manufacture of Traditional Italian Cheeses at the Same Dairy Plant. Food Microbiol. 2016, 54, 115–126. [Google Scholar] [CrossRef]
  132. Sun, L.; D’Amico, D.J. Composition, Succession, and Source Tracking of Microbial Communities throughout the Traditional Production of a Farmstead Cheese. mSystems 2021, 6, e00830-21. [Google Scholar] [CrossRef]
  133. Knights, D.; Kuczynski, J.; Charlson, E.S.; Zaneveld, J.; Mozer, M.C.; Collman, R.G.; Bushman, F.D.; Knight, R.; Kelley, S.T. Bayesian Community-Wide Culture-Independent Microbial Source Tracking. Nat. Methods 2011, 8, 761–763. [Google Scholar] [CrossRef][Green Version]
  134. Montel, M.-C.; Buchin, S.; Mallet, A.; Delbes-Paus, C.; Vuitton, D.A.; Desmasures, N.; Berthier, F. Traditional Cheeses: Rich and Diverse Microbiota with Associated Benefits. Int. J. Food Microbiol. 2014, 177, 136–154. [Google Scholar] [CrossRef]
  135. Busetta, G.; Garofalo, G.; Mangione, G.; Botta, L.; Franciosi, E.; Di Gerlando, R.; Todaro, M.; Licitra, G.; Scatassa, M.L.; Gaglio, R.; et al. Polyphasic Characterization of Microbiota of “Mastredda”, a Traditional Wooden Tool Used during the Production of PDO Provola Dei Nebrodi Cheese. Appl. Sci. 2021, 11, 8647. [Google Scholar] [CrossRef]
  136. Zago, M.; Bardelli, T.; Rossetti, L.; Nazzicari, N.; Carminati, D.; Galli, A.; Giraffa, G. Evaluation of Bacterial Communities of Grana Padano Cheese by DNA Metabarcoding and DNA Fingerprinting Analysis. Food Microbiol. 2021, 93, 103613. [Google Scholar] [CrossRef] [PubMed]
  137. Kamimura, B.A.; De Filippis, F.; Sant’Ana, A.S.; Ercolini, D. Large-Scale Mapping of Microbial Diversity in Artisanal Brazilian Cheeses. Food Microbiol. 2019, 80, 40–49. [Google Scholar] [CrossRef]
  138. Sant’Anna, F.M.; Wetzels, S.U.; Cicco, S.H.S.; Figueiredo, R.C.; Sales, G.A.; Figueiredo, N.C.; Nunes, C.A.; Schmitz-Esser, S.; Mann, E.; Wagner, M.; et al. Microbial Shifts in Minas Artisanal Cheeses from the Serra Do Salitre Region of Minas Gerais, Brazil throughout Ripening Time. Food Microbiol. 2019, 82, 349–362. [Google Scholar] [CrossRef]
  139. Li, J.; Zheng, Y.; Xu, H.; Xi, X.; Hou, Q.; Feng, S.; Wuri, L.; Bian, Y.; Yu, Z.; Kwok, L.-Y.; et al. Bacterial Microbiota of Kazakhstan Cheese Revealed by Single Molecule Real Time (SMRT) Sequencing and Its Comparison with Belgian, Kalmykian and Italian Artisanal Cheeses. BMC Microbiol. 2017, 17, 13. [Google Scholar] [CrossRef] [PubMed][Green Version]
  140. Lucchini, R.; Cardazzo, B.; Carraro, L.; Negrinotti, M.; Balzan, S.; Novelli, E.; Fasolato, L.; Fasoli, F.; Farina, G. Contribution of Natural Milk Culture to Microbiota, Safety and Hygiene of Raw Milk Cheese Produced in Alpine Malga. Ital. J. Food Saf. 2018, 7, 6967. [Google Scholar] [CrossRef]
  141. Xue, J.; Yang, Y.; Wang, Z.; Guo, Y.; Shao, Y. Bacterial Diversity in Chinese Rushan Cheese From Different Geographical Origins. Front. Microbiol. 2018, 9, 1920. [Google Scholar] [CrossRef]
  142. Johnson, J.; Curtin, C.; Waite-Cusic, J. The Cheese Production Facility Microbiome Exhibits Temporal and Spatial Variability. Front. Microbiol. 2021, 12, 644828. [Google Scholar] [CrossRef]
  143. Kamilari, E.; Anagnostopoulos, D.A.; Papademas, P.; Kamilaris, A.; Tsaltas, D. Characterizing Halloumi Cheese’s Bacterial Communities through Metagenomic Analysis. LWT 2020, 126, 109298. [Google Scholar] [CrossRef][Green Version]
  144. Aldrete-Tapia, A.; Escobar-Ramírez, M.C.; Tamplin, M.L.; Hernández-Iturriaga, M. High-Throughput Sequencing of Microbial Communities in Poro Cheese, an Artisanal Mexican Cheese. Food Microbiol. 2014, 44, 136–141. [Google Scholar] [CrossRef]
  145. Porcellato, D.; Skeie, S.B. Bacterial Dynamics and Functional Analysis of Microbial Metagenomes during Ripening of Dutch-Type Cheese. Int. Dairy J. 2016, 61, 182–188. [Google Scholar] [CrossRef]
  146. Cardinali, F.; Ferrocino, I.; Milanović, V.; Belleggia, L.; Corvaglia, M.R.; Garofalo, C.; Foligni, R.; Mannozzi, C.; Mozzon, M.; Cocolin, L.; et al. Microbial Communities and Volatile Profile of Queijo de Azeitão PDO Cheese, a Traditional Mediterranean Thistle-Curdled Cheese from Portugal. Food Res. Int. 2021, 147, 110537. [Google Scholar] [CrossRef] [PubMed]
  147. Biolcati, F.; Bottero, M.T.; Dalmasso, A. Microbiological Analysis of the Robiola Di Roccaverano Cheese by Means of Traditional Culturing Methods. Ital. J. Food Saf. 2019, 8. [Google Scholar] [CrossRef] [PubMed]
  148. Martín, J.F.; Coton, M. Blue cheese: Microbiota and fungal metabolites. In Fermented Foods in Health and Disease Prevention; Academic Press: San Diego, CA, USA, 2017; pp. 275–303. [Google Scholar] [CrossRef]
  149. Coton, E.; Coton, M.; Hymery, N.; Mounier, J.; Jany, J.-L. Penicillium Roqueforti: An Overview of Its Genetics, Physiology, Metabolism and Biotechnological Applications. Fungal Biol. Rev. 2020, 34, 59–73. [Google Scholar] [CrossRef]
  150. Gillot, G.; Jany, J.-L.; Poirier, E.; Maillard, M.-B.; Debaets, S.; Thierry, A.; Coton, E.; Coton, M. Functional Diversity within the Penicillium Roqueforti Species. Int. J. Food Microbiol. 2017, 241, 141–150. [Google Scholar] [CrossRef]
  151. Garnier, L.; Valence, F.; Mounier, J. Diversity and Control of Spoilage Fungi in Dairy Products: An Update. Microorganisms 2017, 5, 42. [Google Scholar] [CrossRef]
  152. Mounier, J.; Coton, M. Kluyveromyces spp. In Encyclopedia of Dairy Sciences; McSweeney, P.L.H., McNamara, J.P., Eds.; Elsevier: San Diego, CA, USA, 2022; Volume 4, pp. 569–574. [Google Scholar]
  153. Lundin, D.; Severin, I.; Logue, J.B.; Östman, Ö.; Andersson, A.F.; Lindström, E.S. Which Sequencing Depth Is Sufficient to Describe Patterns in Bacterial α- and β-Diversity? Sequencing Depth in Diversity Research. Environ. Microbiol. Rep. 2012, 4, 367–372. [Google Scholar] [CrossRef]
  154. Rubin, B.E.R.; Sanders, J.G.; Hampton-Marcell, J.; Owens, S.M.; Gilbert, J.A.; Moreau, C.S. DNA Extraction Protocols Cause Differences in 16S RRNA Amplicon Sequencing Efficiency but Not in Community Profile Composition or Structure. MicrobiologyOpen 2014, 3, 910–921. [Google Scholar] [CrossRef][Green Version]
  155. Nearing, J.T.; Douglas, G.M.; Comeau, A.M.; Langille, M.G.I. Denoising the Denoisers: An Independent Evaluation of Microbiome Sequence Error-Correction Approaches. PeerJ 2018, 6, e5364. [Google Scholar] [CrossRef][Green Version]
  156. Unno, R.; Suzuki, T.; Matsutani, M.; Ishikawa, M. Evaluation of the Relationships Between Microbiota and Metabolites in Soft-Type Ripened Cheese Using an Integrated Omics Approach. Front. Microbiol. 2021, 12, 681185. [Google Scholar] [CrossRef]
  157. Piwosz, K.; Shabarova, T.; Pernthaler, J.; Posch, T.; Šimek, K.; Porcal, P.; Salcher, M.M. Bacterial and Eukaryotic Small-Subunit Amplicon Data Do Not Provide a Quantitative Picture of Microbial Communities, but They Are Reliable in the Context of Ecological Interpretations. mSphere 2020, 5, e00052-20. [Google Scholar] [CrossRef][Green Version]
  158. Yeh, Y.-C.; Needham, D.M.; Sieradzki, E.T.; Fuhrman, J.A. Taxon Disappearance from Microbiome Analysis Reinforces the Value of Mock Communities as a Standard in Every Sequencing Run. mSystems 2018, 3, e00023-18. [Google Scholar] [CrossRef][Green Version]
  159. Brumfield, K.D.; Huq, A.; Colwell, R.R.; Olds, J.L.; Leddy, M.B. Microbial Resolution of Whole Genome Shotgun and 16S Amplicon Metagenomic Sequencing Using Publicly Available NEON Data. PLoS ONE 2020, 15, e0228899. [Google Scholar] [CrossRef] [PubMed]
  160. Johnson, J.S.; Spakowicz, D.J.; Hong, B.-Y.; Petersen, L.M.; Demkowicz, P.; Chen, L.; Leopold, S.R.; Hanson, B.M.; Agresta, H.O.; Gerstein, M.; et al. Evaluation of 16S RRNA Gene Sequencing for Species and Strain-Level Microbiome Analysis. Nat. Commun. 2019, 10, 5029. [Google Scholar] [CrossRef][Green Version]
  161. Cassago, A.L.L.; Artêncio, M.M.; de Moura Engracia Giraldi, J.; Da Costa, F.B. Metabolomics as a Marketing Tool for Geographical Indication Products: A Literature Review. Eur. Food Res. Technol. 2021, 247, 2143–2159. [Google Scholar] [CrossRef]
  162. Ranjan, R.; Rani, A.; Metwally, A.; McGee, H.S.; Perkins, D.L. Analysis of the Microbiome: Advantages of Whole Genome Shotgun versus 16S Amplicon Sequencing. Biochem. Biophys. Res. Commun. 2016, 469, 967–977. [Google Scholar] [CrossRef]
  163. Quince, C.; Walker, A.W.; Simpson, J.T.; Loman, N.J.; Segata, N. Shotgun Metagenomics, from Sampling to Analysis. Nat. Biotechnol. 2017, 35, 833–844. [Google Scholar] [CrossRef][Green Version]
  164. Beghini, F.; McIver, L.J.; Blanco-Míguez, A.; Dubois, L.; Asnicar, F.; Maharjan, S.; Mailyan, A.; Manghi, P.; Scholz, M.; Thomas, A.M.; et al. Integrating Taxonomic, Functional, and Strain-Level Profiling of Diverse Microbial Communities with BioBakery 3. eLife 2021, 10, e65088. [Google Scholar] [CrossRef] [PubMed]
  165. Mahony, J.; van Sinderen, D. Virome Studies of Food Production Systems: Time for ‘Farm to Fork’ Analyses. Curr. Opin. Biotechnol. 2022, 73, 22–27. [Google Scholar] [CrossRef]
  166. Afshari, R.; Pillidge, C.J.; Read, E.; Rochfort, S.; Dias, D.A.; Osborn, A.M.; Gill, H. New Insights into Cheddar Cheese Microbiota-Metabolome Relationships Revealed by Integrative Analysis of Multi-Omics Data. Sci. Rep. 2020, 10, 3164. [Google Scholar] [CrossRef] [PubMed][Green Version]
  167. Goyal, K.; Kumar, P.; Verma, K. Food Adulteration Detection Using Artificial Intelligence: A Systematic Review. Arch. Comput. Methods Eng. 2022, 29, 397–426. [Google Scholar] [CrossRef]
Figure 1. Technological factors affecting cheese microbiota biodiversity. Green and purple lines show combinations of technological factors during cheese-making that increase or decrease this biodiversity.
Figure 1. Technological factors affecting cheese microbiota biodiversity. Green and purple lines show combinations of technological factors during cheese-making that increase or decrease this biodiversity.
Foods 11 03379 g001
Table 1. Advantages and limitations of stable isotope ratio mass spectrometry for cheese origin authentication.
Table 1. Advantages and limitations of stable isotope ratio mass spectrometry for cheese origin authentication.
Isotope ratio such as 18O, 2H, and 34S have a predictable and reproducible response toward geographical origin[38]Unclear effect of cheese-making on stable isotope ratio[34,43]
Elevate correct classification rate[42]High operating cost[47]
Consistent accuracy in origin authentication[34,41,42]13C and 15N are highly affected by animal feed[41]
Table 2. Advantages and limitations of inductively coupled plasma-mass spectrometry for cheese origin authentication.
Table 2. Advantages and limitations of inductively coupled plasma-mass spectrometry for cheese origin authentication.
Elemental composition is mainly affected by geological and pedological traits[49]Animal feed and mineral supplementation affect elements’ composition[50]
Low operation costs with good analytical performance[47]Some elements such as Cu2+ and Zn2+ are highly affected by dairy equipment[52]
Fast and multi-element analysis[44]Requires careful sample preparation[39]
Table 3. Advantages and limitations of infrared spectroscopy for cheese origin authentication.
Table 3. Advantages and limitations of infrared spectroscopy for cheese origin authentication.
May identify peculiar spectral differences connected with cheese origin[57]Requires large sample size for calibration[64]
Fast, solvent-free, automatic, non-destructive, non-invasive, inexpensive, and can be used as a multiparameter analysis[49]Fingerprints may not detect less concentrated molecules connected with geographical origin[57]
Minimal sample preparation[58]Lack of studies on the analysis and quantification the main sources of absorbance variation at each wavelength for cheese[65]
Table 4. Advantages and limitations of nuclear magnetic resonance for cheese origin authentication.
Table 4. Advantages and limitations of nuclear magnetic resonance for cheese origin authentication.
Allows to obtain detailed information on cheese metabolites[44]Extremely high cost of acquisition[73]
May identify biomarkers related to geographical origin[73]High detection limits[74]
Minimal sample preparation[49]Complex calibration[73]
Table 5. Advantages and limitations of volatilome analysis for cheese origin authentication.
Table 5. Advantages and limitations of volatilome analysis for cheese origin authentication.
High resolution, short separation time, high sensitivity, and low cost[32]High dependency on ripening time[103,104]
Measures correlated with cheese quality[87]Highly impacted by extraction methods[89]
Fingerprint profile with discrete correct classification rate[92,105]High variability of correct classification rate[37,85,92]
Table 6. Advantages and limitations of HTS amplicon sequencing for cheese origin authentication.
Table 6. Advantages and limitations of HTS amplicon sequencing for cheese origin authentication.
Time- and cost-effective processing of large sample numbers[153]Analyses could be biased by sample processing, DNA extraction methods, and equimolar library preparation[154]
Consolidated pipeline for data analysis[155]PCR amplification steps include errors, e.g., PCR specificity and variation of 16S rRNA copy number per genome[108]
Identification of taxonomic groups associated with typical flavor and cheese-making technology[156]Under- or over-estimation of microbial community diversity[112,155]
Allows improvement of cheese-making to ensure safety while preserving typicity[137]Lack of absolute abundance[157,158]
Evaluation of core microbiome describing facility-associated microbial groups[130,131]Limited and uneven taxonomic resolutions[159,160]
May pinpoint new biotypes[143]DNA amplicon sequencing typically does not discriminate between live and dead microorganisms (except if DNA stains such as propidium monoazide are used)[87]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Cardin, M.; Cardazzo, B.; Mounier, J.; Novelli, E.; Coton, M.; Coton, E. Authenticity and Typicity of Traditional Cheeses: A Review on Geographical Origin Authentication Methods. Foods 2022, 11, 3379.

AMA Style

Cardin M, Cardazzo B, Mounier J, Novelli E, Coton M, Coton E. Authenticity and Typicity of Traditional Cheeses: A Review on Geographical Origin Authentication Methods. Foods. 2022; 11(21):3379.

Chicago/Turabian Style

Cardin, Marco, Barbara Cardazzo, Jérôme Mounier, Enrico Novelli, Monika Coton, and Emmanuel Coton. 2022. "Authenticity and Typicity of Traditional Cheeses: A Review on Geographical Origin Authentication Methods" Foods 11, no. 21: 3379.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop