Identification of the Geographic Origin of Parmigiano Reggiano (P.D.O.) Cheeses Deploying Non-Targeted Mass Spectrometry and Chemometrics

1H-NMR Approach for the Discrimination of PDO Grana Padano Cheese from Non-PDO Cheeses

Protected Designation of Origin cheeses are products with high-quality standards that can claim higher prices on the market. For this reason, non-PDO cheeses with lower quality can be mislabeled as PDO or mixed with it for economic gain especially when the product is in a shredded form. Luckily, the production of PDO cheese is subjected to strict procedural specification rules that result in a product with a defined profile of its metabolites, which can be used for authentication purposes. In this study, an NMR metabolomic approach combined with multivariate analysis was implemented to build a classification model able to discriminate PDO Grana Padano cheese from a large dataset of competitors. The great advantage of the proposed approach is a simple sample preparation, obtaining a holistic overview of the analyzed samples. The untargeted approach highlighted a "typical profile" of Grana Padano samples, which could be used for protection purposes. In parallel, the targeted results allowed us to identify potential chemicals, such as lactate, some amino acids and lipids. These initial results could open the road to a potential new additional tool to check the authenticity of PDO cheeses in the future.

Tracing the identity of Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" cheese using NMR spectroscopy and multivariate data analysis

BACKGROUND

Nuclear magnetic resonance (NMR) spectroscopy is one of the well-established tools for food metabolomic analysis, as it proved to be very effective in authenticity and quality control of dairy products, as well as to follow product evolution during processing and storage. The analytical assessment of the EU mountain denomination label, specifically for Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" (Mountain-CQ) cheese, has received limited attention. Although it was established in 2012 the EU mountain denomination label has not been much studied from an analytical point of view. Nonetheless, tracing a specific profile for the mountain products is essential to support the value chain of this specialty.

RESULTS

The aim of the study was to produce an identity profile for Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" (Mountain-CQ) cheese, and to differentiate it from Parmigiano Reggiano PDO samples (conventional-PDO) using 1H NMR spectroscopy coupled with multivariate data analysis. Three different approaches were applied and compared. First, the spectra-as-such were analysed after proper preprocessing. For the other two approaches, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) was used for signals resolution and features extraction, either individually on manually-defined spectral intervals or by reapplying MCR-ALS on the whole spectra with selectivity constraints using the reconstructed "pure profiles" as initial estimates and targets. All approaches provided comparable information regarding the samples' distribution, as in all three cases the separation between the two product categories conventional-PDO and Mountain-CQ could be highlighted. Moreover, a novel MATLAB toolbox for features extraction via MCR-ALS was developed and used in synergy with the Chenomx library, allowing for a putative identification of the selected features.

SIGNIFICANCE

A first identity profile for Parmigiano Reggiano "Prodotto di Montagna - Progetto Territorio" obtained by interpreting the metabolites signals in NMR spectroscopy was obtained. Our workflow and toolbox for generating the features dataset allows a more straightforward interpretation of the results, to overcome the limitations due to dimensionality and to peaks overlapping, but also to include the signals assignment and matching since the early stages of the data processing and analysis.

Untargeted metabolomics liquid chromatography-high resolution mass spectrometry approach for the geographical origin assessment of Italian dehydrated apples

Geographical provenience is nowadays a relevant aspect of the authenticity and the quality of many food commodities. Dehydrated apple cubes/slices represent an ingredient commonly used by food companies for bakery products. However, this apple-based matrix is not so known and studied from an analytical point of view. In the present work, seven compounds were identified as key molecules to distinguish between Italian and non-Italian samples, through an untargeted ultrahigh-pressure liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) approach. This methodology was merged with multivariate statistical analysis, and the principal features were studied and identified considering several identification steps. Samples from 2020 and 2021 harvesting campaigns, with partial and total dehydration rates, with or without peel, and from different apple varieties were considered for the study, for a total of 91 samples. Afterward, the same analysis protocol was applied to an external set (n = 12 samples), included in the statistical models, searching for the key compounds identified in the training set. Interesting and significant results underlined the potentiality of the UHPLC-HRMS technology as a confirmatory strategy for the geographical origin assessment of dehydrated apple commodities.

Rapid Food Authentication Using a Portable Laser-Induced Breakdown Spectroscopy System

Laser-induced breakdown spectroscopy (LIBS) is an atomic-emission spectroscopy technique that employs a focused laser beam to produce microplasma. Although LIBS was designed for applications in the field of materials science, it has lately been proposed as a method for the compositional analysis of agricultural goods. We deployed commercial handheld LIBS equipment to illustrate the performance of this promising optical technology in the context of food authentication, as the growing incidence of food fraud necessitates the development of novel portable methods for detection. We focused on regional agricultural commodities such as European Alpine-style cheeses, coffee, spices, balsamic vinegar, and vanilla extracts. Liquid examples, including seven balsamic vinegar products and six representatives of vanilla extract, were measured on a nitrocellulose membrane. No sample preparation was required for solid foods, which consisted of seven brands of coffee beans, sixteen varieties of Alpine-style cheeses, and eight different spices. The pre-processed and standardized LIBS spectra were used to train and test the elastic net-regularized multinomial classifier. The performance of the portable and benchtop LIBS systems was compared and described. The results indicate that field-deployable, portable LIBS devices provide a robust, accurate, and simple-to-use platform for agricultural product verification that requires minimal sample preparation, if any.

Food inauthenticity: Authority activities, guidance for food operators, and mitigation tools

Historically, food fraud was a major public health concern which helped drive the development of early food regulations in many markets including the US and EU market. In the past 10 years, the integrity of food chains with respect to food fraud has again been questioned due to high profile food fraud cases. We provide an overview of the resulting numerous authoritative activities underway within different regions to counter food fraud, and we describe the guidance available to the industry to understand how to assess the vulnerability of their businesses and implement appropriate mitigation. We describe how such controls should be an extension of those already in place to manage wider aspects of food authenticity, and we provide an overview of relevant analytical tools available to food operators and authorities to protect supply chains. Practical Application: Practical Application of the provided information by the food industry in selecting resources (guidance document, analytical methods etc.).

Long-Aged Parmigiano Reggiano PDO: Trace Element Determination Targeted to Health

The concentrations of four health-related trace elements were measured using Atomic Absorption Spectroscopy in long-ripened (24- and 40-months) Parmigiano Reggiano (PR) PDO cheese, obtained from both summer and winter milk. To date, there are limited data on PR trace element concentrations, and no data about long-ripened cheese, especially when ripened for 40 months. Thus, the aim of this investigation is to determine chromium, manganese, selenium, and zinc concentrations, improving the available data on these trace elements and increasing knowledge of the biological properties of PR linked to their content in this cheese. The results show that 40-month ripened PR is a source of selenium and chromium, according to definitions under the European Regulation 1924/2006, as a 30 g cheese portion contains 11 ± 2 μg (summer milk) and 10 ± 1 μg (winter milk) of selenium and 8 ± 1 μg (summer and winter milk) of chromium, providing in excess of 8.25 and 6 μg per portion, respectively. This represents 15% of nutrient reference intake values for adults. These findings allow for the claim to be made that PR possesses the health properties ascribed to food sources of selenium and chromium according to European Regulation 432/2012.

A combined metabolomics and peptidomics approach to discriminate anomalous rind inclusion levels in Parmigiano Reggiano PDO grated hard cheese from different ripening stages

Parmigiano Reggiano is a hard cheese with a Protected Designation of Origin (PDO) certification that also applies to the grated product. The percentage of rind in grated Parmigiano Reggiano is regulated by the PDO production Specification and must not exceed the limit of 18% (w/w). The present study evaluates the potential of an untargeted foodomics approach to detect anomalous inclusions of rind in grated Parmigiano Reggiano cheese. In particular, a combined metabolomics and peptidomics approach was used to detect potential markers of counterfeits (rind > 18%). In the framework of realistic food integrity purposes, non-Parmigiano Reggiano grated samples and different ripening times were also considered. Untargeted metabolomics allowed detecting 347 compounds, with a prevalence of amino acids and peptide derivatives, followed by fatty acyls and other compounds (such as lactones, ketones, and aldehydes) typically related to proteolysis and lipolysis events. Overall, the unsupervised multivariate statistics showed that the ripening time plays a hierarchically higher impact than rind inclusion in determining the main differences in the chemical profiles detected. Interestingly, supervised statistics highlighted distinctive markers for ripening time and rind inclusion, with only 16 common discriminant compounds being shared between the two conditions. The best markers of rind inclusion > 18% were 2-hydroxyadenine (VIP score = 1.937; AUC value = 0.83) and the amino acid derivatives argininic acid (VIP score = 1.462; AUC value = 0.75) and 5-hydroxyindole acetaldehyde (VIP score = 1.710; AUC value = 0.86). Interestingly, the medium-chain aldehyde 4-hydroperoxy-2-nonenal was a common marker of both ripening time and anomalous rind inclusion (>18%), likely arising from the lipid oxidation processes. Finally, among potential marker peptides of rind inclusion, the alpha-S1 casein proteolytic product (F)FVAPFPEVFGK(E) could be identified.

An Array of MOX Sensors and ANNs to Assess Grated Parmigiano Reggiano Cheese Packs' Compliance with CFPR Guidelines

Parmigiano Reggiano cheese is one of the most appreciated Italian foods on account of its high nutrient content and taste. Due to its high cost, these characteristics make this product subject to counterfeiting in different forms. In this study, an approach based on an array of gas sensors has been employed to assess if it was possible to distinguish different samples based on their aroma. Samples were characterized in terms of rind percentage, seasoning, and rind working process. From the responses of the sensors, five features were extracted and the capability of these parameters to recognize target classes was tested with statistical analysis. Hence, the performance of the sensors’ array was quantified using artificial neural networks. To simplify the problem, a hierarchical approach has been used: three steps of classification were performed, and in each step one parameter of the grated cheese was identified (firstly, seasoning; secondly, rind working process; finally, rind percentage). The accuracies ranged from 88.24% to 100%.

Low-Power Detection of Food Preservatives by a Novel Nanowire-Based Sensor Array

Food preservatives are compounds that are used for the treatment of food to improve the shelf life. In the food industry, it is necessary to monitor all processes for both safety and quality of the product. An electronic nose (or e-nose) is a biomimetic olfactory system that could find numerous industrial applications, including food quality control. Commercial electronic noses are based on sensor arrays composed by a combination of different sensors, which include conductometric metal oxide devices. Metal oxide nanowires are considered among the most promising materials for the fabrication of novel sensing devices, which can enhance the overall performances of e-noses in food applications. The present work reports the fabrication of a novel sensor array based on SnO2, CuO, and WO3 nanowires deposited on top of μHPs provided by ams Sensor Solutions Germany GmbH. The array was tested for the discrimination of four typical compounds added to food products or used for their treatment to increase the shelf life: ethanol, acetone, nitrogen dioxide, and ozone. Results are very promising; the sensors array was able to operate for a long time, consuming less than 50 mW for each single sensor, and principal component analysis (PCA) confirmed that the device was able to discriminate between different compounds.

Colonization of the human gut by bovine bacteria present in Parmesan cheese

The abilities of certain microorganisms to be transferred across the food production chain, persist in the final product and, potentially, colonize the human gut are poorly understood. Here, we provide strain-level evidence supporting that dairy cattle-associated bacteria can be transferred to the human gut via consumption of Parmesan cheese. We characterize the microbial communities in samples taken from five different locations across the Parmesan cheese production chain, confirming that the final product contains microorganisms derived from cattle gut, milk, and the nearby environment. In addition, we carry out a human pilot study showing that Bifidobacterium mongoliense strains from cheese can transiently colonize the human gut, a process that can be enhanced by cow milk consumption.

Some microorganisms may be transferred across the food production chain and, potentially, colonize the human gut. Here, Milani et al. provide strain-level evidence supporting that dairy cattle-associated bacteria can be transferred to the human gut via consumption of Parmesan cheese.

Application of a Novel S3 Nanowire Gas Sensor Device in Parallel with GC-MS for the Identification of Rind Percentage of Grated Parmigiano Reggiano

Parmigiano Reggiano cheese is one of the most appreciated and consumed foods worldwide, especially in Italy, for its high content of nutrients and taste. However, these characteristics make this product subject to counterfeiting in different forms. In this study, a novel method based on an electronic nose has been developed to investigate the potentiality of this tool to distinguish rind percentages in grated Parmigiano Reggiano packages that should be lower than 18%. Different samples, in terms of percentage, seasoning and rind working process, were considered to tackle the problem at 360°. In parallel, GC-MS technique was used to give a name to the compounds that characterize Parmigiano and to relate them to sensors responses. Data analysis consisted of two stages: Multivariate analysis (PLS) and classification made in a hierarchical way with PLS-DA ad ANNs. Results were promising, in terms of correct classification of the samples. The correct classification rate (%) was higher for ANNs than PLS-DA, with correct identification approaching 100 percent.