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

Use of NDSS to discriminate between Parmigiano Reggiano and Grana Padano PDO and their ripening times from grated cheese spectra

This study focuses on using spectral data recorded with two different Near-Infrared (NIR) instruments (a benchtop and a portable device) to differentiate between Parmigiano Reggiano (PR) and Grana Padano (GP) Protected Designation of Origin (PDO) cheeses and their ripening times. Key findings showed that NIR spectroscopy effectively discriminated between PR and GP, with spectral range differences linked to their chemical composition, including fat, protein, and carbohydrate content. Specifically, certain wavelength ranges (1375-1400 nm, 1205-1250 nm, and 1410-1440 nm) were identified as significant in distinguishing PDO labels, highlighting the roles of fat and protein content in the cheese classification. Spectral features are also distinguished between ripening times, with specific wavelength bands tied to biochemical modifications during maturation, such as changes in moisture, protein, and fat content. In terms of instrument performance, the benchtop device achieved high accuracy in PDO classification (up to 0.97 F1 score), particularly when using a first derivative pre-treatment. The portable device performances showed higher variability but performed flawlessly for PDO classification. While both instruments effectively classified cheeses of distinct ripening ages, they were less successful at detecting samples containing mixtures of different aged cheeses. The portable instrument showed better results when combining the visible spectrum (350-950 nm) with the NIR spectrum (950-1650 nm), capturing surface color changes alongside internal structural transformations related to aging. Overall, the study validates the potential of NIR spectroscopy, especially when combined with established preprocessing techniques, as a powerful non-destructive tool to authenticate specific cheese PDO and assess ripening stages.

Natural creaming significantly modulates the metabolomic profile and bacterial community of raw milk: A case study on organic milk for Parmigiano Reggiano PDO

This study analyzed raw milk's microbial community and chemical profile during the natural creaming process of Parmigiano Reggiano production by comparing milk from farms following two different organic certifications. Specifically, the natural creaming process underlined the positive accumulation of potentially pro-dairy bacteria, particularly those of the genera Lactococcus and Streptococcus, and a significant reduction of negative bacterial genera, such as Acinetobacter and Rothia, in the final mix milk. Meanwhile, untargeted metabolomic analysis confirmed the representativeness of lipids and lipid-derivatives as chemical markers involved in the overnight creaming process, with fatty acid esters and long fatty acids enriched in the evening samples. Finally, by using a multi-omics approach, we integrated microbial and metabolomic datasets and identified correlations between specific microbial populations and metabolite changes. This integrative analysis revealed microbial-metabolite interactions that may be a starting point to better understand the pivotal role exerted by milk creaming on the final cheese quality.

Integration of LC-HRMS and 1H NMR metabolomics data fusion approaches for classification of Amarone wine based on withering time and yeast strain

Two untargeted metabolomics approaches (LC-HRMS and 1H NMR) were combined to classify Amarone wines based on grape withering time and yeast strain. The study employed a multi-omics data integration approach, combining unsupervised data exploration (MCIA) and supervised statistical analysis (sPLS-DA). The results revealed that the multi-omics pseudo-eigenvalue space highlighted a limited correlation between the datasets (RV-score = 16.4%), suggesting the complementarity of the assays. Furthermore, the sPLS-DA models correctly classified wine samples according to both withering time and yeast strains, providing a much broader characterization of wine metabolome with respect to what was obtained from the individual techniques. Significant variations were notably observed in the accumulation of amino acids, monosaccharides, and polyphenolic compounds throughout the withering process, with a lower error rate in sample classification (7.52%). In conclusion, this strategy demonstrated a high capability to integrate large omics datasets and identify key metabolites able to discriminate wine samples based on their characteristics.

Metabolomic profiling of Fiore Sardo cheese: Investigation of the influence of thermal treatment and ripening time using univariate and multivariate classification techniques

The effect of different sub-pasteurization heat treatments and different ripening times was investigated in this work. The metabolite profiles of 95 cheese samples were analyzed using GC-MS in order to determine the effects of thermal treatment (raw milk, 57 °C and 68 °C milk thermization) and ripening time (105 and 180 days). ANOVA test on GC-MS peaks complemented with false discovery rate correction was employed to identify the compounds whose levels significantly varied over different ripening times and thermal treatments. The univariate t-test classifier and Partial Least Square Discriminant Analysis (PLS-DA) provided acceptable classification results, with an overall accuracy in cross-validation of 76% for the univariate model and 72% from the PLS-DA. The metabolites that mostly changed with ripening time were amino acids and one endocannabinoid (i.e., arachidonoyl amide), while compounds belonging to the classes of biogenic amines and saccharides resulted in being strongly affected by the thermization process.

Untargeted metabolomics and machine learning unveil quality and authenticity interactions in grated Parmigiano Reggiano PDO cheese

The chemical composition of Parmigiano Reggiano (PR) hard cheese can be significantly affected by different factors across the dairy supply chain, including ripening, altimetric zone, and rind inclusion levels in grated hard cheeses. The present study proposes an untargeted metabolomics approach combined with machine learning chemometrics to evaluate the combined effect of these three critical parameters. Specifically, ripening was found to exert a pivotal role in defining the signature of PR cheeses, with amino acids and lipid derivatives that exhibited their role as key discriminant compounds. In parallel, a random forest classifier was used to predict the rind inclusion levels (> 18%) in grated cheeses and to authenticate the specific effect of altimetry dairy production, achieving a high prediction ability in both model performances (i.e., ∼60% and > 90%, respectively). Overall, these results open a novel perspective to identifying quality and authenticity markers metabolites in cheese.

Implementation of plant extracts for cheddar-type cheese production in conjunction with FTIR and Raman spectroscopy comparison

Plant extracts have demonstrated the ability to act as coagulants for milk coagulation at an adequate concentration, wide temperatures and pH ranges. This research is focused on the use of different vegetative extracts such as Citrus aurnatium flower extract (CAFE), bromelain, fig latex, and melon extract as economical and beneficial coagulants in the development of plant-based cheddar-type cheese. The cheddar-type cheese samples were subjected to physicochemical analysis in comparison to controlled cheese samples made from acetic acid and rennet. The fat, moisture, protein, and salt contents remained the same over the storage period, but a slight decline was observed in pH. The Ferric reducing antioxidant power (FRAP) increased with the passage of the ripening period. The FTIR and Raman spectra showed exponential changes and qualitative estimates in the binding and vibrational structure of lipids and protein in plant-based cheeses. The higher FTIR and Raman spectra bands were observed in acid, rennet, bromelain, and CAFE due to their firm and strong texture of cheese while lower spectra were observed in cheese made from melon extract due to weak curdling and textural properties. These plant extracts are economical and easily available alternative sources for cheese production with higher protein and nutritional contents.

Current Challenges and Future Directions in Peptidomics

The field of peptidomics has been under development since its start more than 20 years ago. In this chapter we provide a personal outlook for future directions in this field. The applications of peptidomics technologies are spreading more and more from classical research of peptide hormones and neuropeptides towards commercial applications in plant and food-science. Many clinical applications have been developed to analyze the complexity of biofluids, which are being addressed with new instrumentation, automization, and data processing. Additionally, the newly developed field of immunopeptidomics is showing promise for cancer therapies. In conclusion, peptidomics will continue delivering important information in classical fields like neuropeptides and peptide hormones, benefiting from improvements in state-of-the-art technologies. Moreover, new directions of research such as immunopeptidomics will further complement classical omics technologies and may become routine clinical procedures. Taken together, discoveries of new substances, networks, and applications of peptides can be expected in different disciplines.

Untargeted metabolomics provide new insights into the implication of Lactobacillus helveticus strains isolated from natural whey starter in methylglyoxal-mediated browning

Hard cheeses may occasionally show a brown discolouration during ripening due to multifactorial phenomena that involve bacteria and give rise to pyrazines arising from methylglyoxal. The present work aimed at developing a novel approach to investigate the role of natural starters in browning. To this object, 11 strains of L. helveticus were incubated in a medium containing 10 % rennet casein dissolved in whey, and then growth was monitored by measuring pH and number of genomes/mL. Browning was assessed through CIELab analysis, methylglyoxal production was determined by targeted mass spectrometry, and untargeted metabolomics was used to extrapolate marker compounds associated with browning discoloration. The medium allowed the growth of all the strains tested and differences in colour were observed, especially for strain A7 (ΔE* value 15.92 ± 0.27). Noteworthy, this strain was also the higher producer of methylglyoxal (2.44 µg/mL). Metabolomics highlighted pyrazines and β-carboline compounds as markers of browning at 42 °C and 16 °C, respectively. Moreover, multivariate statistics pointed out differences in free amino acids and oligopeptides linked to proteolysis, while 1,2-propanediol and S-Lactoylglutathione suggested specific detoxification route in methylglyoxal-producing strains. Our model allowed detecting differences in browning amid strains, paving the way towards the study of individual L. helveticus strains to identify the variables leading to discoloration or to study the interaction between different strains in natural whey starters.

Untargeted metabolomics and conventional quality characterization of rowanberry pomace ingredients in meatballs

In this study, a rowanberry pomace defatted with supercritical CO2 (2%-AC), its ethanolic extract (1%-E) and extraction residue (2%-R), were tested in meatball preparation. The meatballs with 1%-E demonstrated the highest in vitro radical scavenging capacity. In the case of 1%-E the pH of meatballs was significantly lower compared to the control sample (P = 0.0132) on the 5-day. The lowest cooking loss was achieved when the meatballs contained mainly fibre-rich 2%-R. The UHPLC method detected 184 metabolites, including strong antioxidants, such as chlorogenic acids, 3',4'-methylenedioxy-5,7-dimethylepicatechin, hyperin, isoquercitrin. The 1%-E was particularly effective against the development of unpleasant off-flavours caused by carbonyl compounds. Consistently, the decrease in lipid oxidation, indicated by reduced 7-dodecenal and 2,4-heptadienal contents, has been observed following the addition of rowanberry extract to meatballs. Metabolomics coupled with conventional quality evaluations provided a deeper understanding of the potential utilization and valorisation of different rowanberry pomace extracts as meat ingredients.

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.

The integrated metabolomics and sensory analyses unravel the peculiarities of mountain grassland-based cheese production: The case of Parmigiano Reggiano PDO

Untargeted metabolomics based on ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry was combined with sensory analysis to provide new insights into the impact of the feeding system from mountain regions (grassland deriving from permanent meadows) on the chemical fingerprint of Parmigiano Reggiano PDO hard cheese. In the framework of a representative investigation, two different ripening times (12 and 24 months) were also considered. Multivariate statistics allowed discriminating cheese samples from different feeding regimens according to their metabolomics signatures. Interestingly, mountain grassland-based cheese samples were characterized by a more favourable fatty acid profile, recording also feed-derived compounds (such as terpenoids and linoleic acid derivatives) potentially associated with both beneficial effects on human health and sensory properties. According to the sensory analysis, the impact of herbs and grass enhanced the colour and retro-olfactive complexity of Parmigiano Reggiano PDO cheese, with spicy, umami and intense vegetal aromatic notes representing distinctive features.

Integrated metabolomics and peptidomics to delineate characteristic metabolites in milk fermented with novel Lactiplantibacillus plantarum L3

A novel wild-type Lactiplantibacillus plantarum (L. plantarum) L3 with good fermentation characteristics and protein degradation capacity was isolated from raw milk samples. In this study, the metabolites in milk fermented with L. plantarum L3 were investigated by metabolomic and peptidomics analyses. The metabolomics results revealed that the metabolites in milk fermented with L. plantarum L3 were Thr-Pro, Val-Lys, l-creatine, pyridoxine, and muramic acid, which improved the taste and nutritional qualities of the milk. Moreover, the water-soluble peptides derived from L3 fermented milk exhibited high antioxidant properties and angiotensin I-converting enzyme inhibitory (ACEI) activities. Additionally, 152 peptides were found using liquid chromatography-mass spectrometry (LC-MS/MS). Furthermore, endogenous enzymes secreted by L. plantarum L3 cleaved β- and α-casein to release six ACEI peptides (ACEIPs), nineteen antioxidant peptides (AOPs), and five antimicrobial peptides (AMPS). Overall, these findings could be valuable in improving the quality of fermented milk.

Role of Platinum Nanozymes in the Oxidative Stress Response of Salmonella Typhimurium

Platinum nanoparticles (PtNPs) are being intensively explored as efficient nanozymes due to their biocompatibility coupled with excellent catalytic activities, which make them potential candidates as antimicrobial agents. Their antibacterial efficacy and the precise mechanism of action are, however, still unclear. In this framework, we investigated the oxidative stress response of Salmonella enterica serovar Typhimurium cells when exposed to 5 nm citrate coated PtNPs. Notably, by performing a systematic investigation that combines the use of a knock-out mutant strain 12023 HpxF- with impaired response to ROS (ΔkatE ΔkatG ΔkatN ΔahpCF ΔtsaA) and its respective wild-type strain, growth experiments in both aerobic and anaerobic conditions, and untargeted metabolomic profiling, we were able to disclose the involved antibacterial mechanisms. Interestingly, PtNPs exerted their biocidal effect mainly through their oxidase-like properties, though with limited antibacterial activity on the wild-type strain at high particle concentrations and significantly stronger action on the mutant strain, especially in aerobic conditions. The untargeted metabolomic analyses of oxidative stress markers revealed that 12023 HpxF- was not able to cope with PtNPs-based oxidative stress as efficiently as the parental strain. The observed oxidase-induced effects comprise bacterial membrane damage as well as lipid, glutathione and DNA oxidation. On the other hand, in the presence of exogenous bactericidal agents such as hydrogen peroxide, PtNPs display a protective ROS scavenging action, due to their efficient peroxidase mimicking activity. This mechanistic study can contribute to clarifying the mechanisms of PtNPs and their potential applications as antimicrobial agents.

Evaluation of different blackcurrant seed ingredients in meatballs by using conventional quality assessment and untargeted metabolomics

Blackcurrants are sources of phenolic compounds, such as anthocyanins, possessing strong antioxidant, antimicrobial and antifungal activity. Therefore, the addition of different blackcurrant pomace ingredients may affect the overall meat quality. The actual chemical profile and bioactivities of blackcurrant pomace ingredients may strongly depend on its preparation; for instance, in our study the highest values of the in vitro antioxidant capacity were determined for blackcurrant seeds after supercritical CO₂ extraction. Starting from these background conditions, in this work, we evaluated the ability of three different concentrations (namely 1, 3, and 5% w/w) of blackcurrant (BC) seeds following EtOH/water extraction (BC-AE), before supercritical fluid CO₂ extraction (BC-RS), and after supercritical fluid CO₂ extraction (BC-ASC) to affect different quality parameters of pork meatballs. These latter were stored considering three different time-points, namely 1, 3 and 6 days at 4 °C packed under modified atmosphere (i.e., 70% N₂ and 30% CO₂). Untargeted metabolomics allowed to identify several lipid and protein-related oxidation products involved in redox reactions, such as 13-L-hydroperoxylinoleic acid, (12S,13S)-epoxylinolenic acid, 9,10-epoxyoctadecenoic acid, glutathione, glutathione disulfide, L-carnosine, l-ascorbic acid, and tocotrienols. Besides, multivariate statistics applied on the metabolomics dataset confirmed that the chemical profile of meatballs was an exclusive combination of both BC inclusion levels and type of BC-ingredients considered. Our findings showed that the higher the concentration of BC seed ingredients in meatballs, the lower the cooking loss and the higher the fibre content. Also, all the ingredients significantly affected the colour parameters.

Brown goat yogurt: Metabolomics, peptidomics, and sensory changes during production

Brown goat milk products have gained popularity for their unique taste and flavor. The emergence of chain-reversal phenomenon makes the design and development of goat milk products gradually tend to a consumer-oriented model. However, the precise mechanism of how browning and fermentation process causes characteristics is not clear. In an effort to understand how the treatments potentially lead to certain metabolite profile changes in goat milk, comprehensive, quantitative metabolomics and peptidomics analysis of goat milk samples after browning and fermentation were undertaken. An intelligent hybrid z-score standardization-principal components algorithm-multimodal denoizing autoencoder was used for feature fusion and hidden layer fusion in high-dimensional variable space. The fermentation process significantly improved the flavor of brown goat yogurt through the tricarboxylic acid-urea-glycolysis composite pathway. Bitter peptides HPFLEWAR, PPGLPDKY, and PPPPPKK have strong interactions with both putative dipeptidyl peptidase IV and angiotensin-converting enzyme, proving that brown goat yogurt can be considered as effective provider of potential putative dipeptidyl peptidase IV and angiotensin-converting enzyme inhibitors. The level of health-promoting bioactive components and sensory contributed to consumer selection. The proposed multimodal data integrative analysis platform was applicable to explain the effect of the dynamic changes of metabolites and peptides on consumer preferences.

Fingerprinting based on gas chromatography-Orbitrap high-resolution mass spectrometry and chemometrics to reveal geographical origin, processing, and volatile markers for thyme authentication

Thyme is an aromatic herb traditionally used for food purposes due to its organoleptic characteristics and medicinal properties, which is highly susceptible to food fraud. In this study, GC-HRMS-based fingerprinting was applied for the first time to determine the geographical traceability of thyme based on different origins (Spain, Poland, and Morocco), as well as to assess its processing by comparing sterilized vs. non-sterilized thyme. Unsupervised chemometric methods (PCA and HCA) revealed a predominant influence of the geographical origin on thyme fingerprints rather than processing effects. Supervised PLS-DA and OPLS-DA were used for discrimination purposes, revealing high predictive ability for further samples (100%), and allowing the identification of differential compounds (markers). A total of 24 markers were putatively identified (13 metabolites were confirmed) belonging to different classes: monoterpenoids, diterpenoids, sesquiterpenoids, alkenylbenzenes, and other miscellaneous compounds. This study outlines the potential of combining untargeted analysis by GC-HRMS with chemometrics for thyme authenticity and traceability.

Assessing food authenticity through protein and metabolic markers

This chapter aims to address an issue of ancient origins, but more and more topical in a globalized world in which consumers and stakeholders are increasingly aware: the authenticity of food. Foods are systems that can also be very complex, and verifying the correspondence between what is declared and the actual characteristics of the product is often a challenging issue. The complexity of the question we want to answer (is the food authentic?) means that the answer is equally articulated and makes use of many different analytical techniques. This chapter will consider the chemical analyses of foods aimed at guaranteeing their authenticity and will focus on frontier methods that have been developed in recent years to address the need to respond to ever-increasing guarantees of authenticity. Targeted and non-targeted approaches will be considered for verifying the authenticity of foods, through the study of different classes of constituents (proteins, metabolites, lipids, flavors). The numerous approaches available (proteomics, metabolomics, lipidomics) and the related analytical techniques (LC-MS, GC-MS, NMR) are first described from a more general point of view, after which their specific application for the purposes of authentication of food is addressed.

Application of an innovative metabolomics approach to discriminate geographical origin and processing of black pepper by untargeted UHPLC-Q-Orbitrap-HRMS analysis and mid-level data fusion

An untargeted metabolomics approach based on ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) fingerprinting was applied to investigate the metabolic differences of black pepper among three geographical origins (Sri Lanka, Vietnam, and Brazil) and two post-harvest processing (sterilized and non-sterilized spice). Principal component analysis (PCA) was employed to assess the overall clustering of samples, whereas supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was effectively used for discrimination purposes. OPLS-DA models were fully validated (R²Y and Q² values > 0.5) and the variable importance in projection (VIP) approach was employed to provide valuable data about differential metabolites with high discrimination potential (8 markers were putatively identified). For origin differentiation, three markers were highlighted with VIP values > 1.5 (i.e. reynosin, artabsinolide D, and tatridin B). Fatty acid derivates were the most frequent markers within the metabolites annotated for processing discrimination (e.g. 10,16-dihydroxyhexadecanoic acid and 9-hydroperoxy-10E-octadecenoic acid). Additionally, different combinations of mid-level data fusion of chromatographic-mass spectrometric techniques (UHPLC and gas chromatography coupled to HRMS) and proton nuclear magnetic resonance spectroscopy (¹H NMR) were evaluated for the first time for geographical and processing discrimination of black pepper. The NMR-UHPLC-GC mid-level fused model was preferred among the tested fusion approaches since good sample clustering and no misclassification were achieved. Enhanced correct classification rate was achieved by mid-level data fusion compared with the findings obtained for one of the individual techniques (¹H NMR fingerprinting) (from 92% to 100% of samples correctly classified). This study opens the path to new metabolomics approaches for black pepper authentication and quality control.

The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in Bryophyllum Medicinal Plants (Genus Kalanchoe)

Phenolic compounds constitute an important family of natural bioactive compounds responsible for the medicinal properties attributed to Bryophyllum plants (genus Kalanchoe, Crassulaceae), but their production by these medicinal plants has not been characterized to date. In this work, a combinatorial approach including plant tissue culture, untargeted metabolomics, and machine learning is proposed to unravel the critical factors behind the biosynthesis of phenolic compounds in these species. The untargeted metabolomics revealed 485 annotated compounds that were produced by three Bryophyllum species cultured in vitro in a genotype and organ-dependent manner. Neurofuzzy logic (NFL) predictive models assessed the significant influence of genotypes and organs and identified the key nutrients from culture media formulations involved in phenolic compound biosynthesis. Sulfate played a critical role in tyrosol and lignan biosynthesis, copper in phenolic acid biosynthesis, calcium in stilbene biosynthesis, and magnesium in flavanol biosynthesis. Flavonol and anthocyanin biosynthesis was not significantly affected by mineral components. As a result, a predictive biosynthetic model for all the Bryophyllum genotypes was proposed. The combination of untargeted metabolomics with machine learning provided a robust approach to achieve the phytochemical characterization of the previously unexplored species belonging to the Bryophyllum subgenus, facilitating their biotechnological exploitation as a promising source of bioactive compounds.