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.

Elderberry (Sambucus nigra L.) Encapsulated Extracts as Meat Extenders against Lipid and Protein Oxidation during the Shelf-Life of Beef Burgers.

In this work, we studied the impact of encapsulated elderberry extracts as natural meat extenders to preserve both the quality and the oxidative stability of beef burgers. In particular, the comprehensive chemical changes of beef burgers treated with different antioxidants, namely, (a) a control without antioxidants, (b) 0.5 g/kg sodium erythorbate (ERY), (c) 2.5 g/kg encapsulated elderberry extract (EE 2.5), and (d) 5 g/kg encapsulated elderberry extract (EE 5), each one packaged under modified atmosphere (80% O2 and 20% CO2) for 13 days storage at 2 ± 1 °C, were deeply evaluated. Overall, EEs showed a wide array of antioxidant compounds, namely polyphenols like anthocyanins, flavonols, and phenolic acids. Multivariate statistics provided marked chemical differences between burgers manufactured with EEs and synthetic antioxidants (ERY) during 13-days storage in terms of both metabolomic profiles and typical lipid/protein oxidation markers (such as malondialdehyde and total carbonyls). Most of the differences could be attributed to some discriminant compounds, namely glutathione, 4-hydroxy-2-nonenal, hydroxy/peroxy-derivatives of fatty acids, carbonyl compounds (such as 5-nonen-2-one and 1,5-octadien-3-one), and cholesterol. Interestingly, significant correlations (p < 0.01) were observed between malondialdehyde, total carbonyls, and these discriminant metabolites. The combination of spectrophotometric approaches and a high-throughput untargeted metabolomics analysis outlined a strong modulation of both lipid and protein oxidations, likely promoted by the encapsulated meat extender (elderberry), thus confirming its ability to delay oxidative phenomena during the shelf-life of beef burgers.

Impact of Pasture-Based Diets on the Untargeted Metabolomics Profile of Sarda Sheep Milk.

In this work, untargeted metabolomics was used to shed light on the impact of different pasture-based diets on the chemical profile of Sarda sheep milk. The study considered 11 dairy sheep farms located in Sardinia, and milk samples were collected in 4 different periods, namely January, March, May, and July 2019, when all sheep had 58, 98, 138, and 178 days in milk, respectively. The animal diet composition was based on the intake of grazed herbage in natural pasture, hay, and concentrate. Overall, the combination of two comprehensive databases on food, namely the Milk Composition Database and Phenol-Explorer, allowed the putative identification of 406 metabolites, with a significant (p < 0.01) enrichment of several metabolite classes, namely amino acids and peptides, monosaccharides, fatty acids, phenylacetic acids, benzoic acids, cinnamic acids, and flavonoids. The multivariate statistical approach based on supervised orthogonal projections to latent structures (OPLS-DA) allowed us to predict the chemical profile of sheep milk samples as a function of the high vs no fresh herbage intake, while the prediction model was not significant when considering both hay and concentrate intake. Among the discriminant markers of the herbage intake, we found five phenolic metabolites (such as hippuric and coumaric acids), together with lutein and cresol (belonging to carotenoids and their metabolites). Additionally, a high discriminant power was outlined for lipid derivatives followed by sugars, amino acids, and peptides. Finally, a pathway analysis revealed that the herbage intake affected mainly five biochemical pathways in milk, namely galactose metabolism, phenylalanine metabolism, alpha-linolenic acid metabolism, linoleic acid metabolism, and aromatic amino acids involved in protein synthesis (namely tyrosine, phenylalanine, and tryptophan).