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Untargeted Metabolomics Combined with Sensory Analysis to Evaluate the Chemical Changes in Coppa Piacentina PDO during Different Ripening Times.

Gabriele RocchettiAlessandra ScansaniGiulia LeniSamantha SigoloTerenzio BertuzziAldo Prandini
Published in: Molecules (Basel, Switzerland) (2023)
Ripening time is known to drive the chemical and sensory profiles of dry meat products, thus potentially affecting the final quality of the product. Starting from these background conditions, the aim of this work was to shed light, for the first time, on the chemical modifications of a typical Italian PDO meat product-namely, Coppa Piacentina-during ripening, to find correlations between its sensory quality and the biomarker compounds related to the progress of ripening. The ripening time (from 60 to 240 days) was found to deeply modify the chemical composition of this typical meat product, providing potential biomarkers of both oxidative reactions and sensory attributes. The chemical analyses revealed that there is typically a significant decrease in the moisture content during ripening, likely due to increased dehydration. In addition, the fatty acid profile showed that the distribution of polyunsaturated fatty acids significantly ( p < 0.05) decreased during ripening, because of their high susceptibility to oxidation and conversion to intermediate and secondary molecules. An untargeted metabolomics approach, coupled with unsupervised and supervised multivariate statistics, highlighted a significant impact (prediction scores > 1) of lipid oxidation during ripening time, with some metabolites (such as γ -glutamyl-peptides, hydroperoxy-fatty acids, and glutathione) being particularly discriminant in predicting the changes observed. The discriminant metabolites were coherent with the progressive increase of peroxide values determined during the entire ripening period. Finally, the sensory analysis outlined that the highest degree of ripening provided greater color intensity of the lean part, slice firmness, and chewing consistency, with glutathione and γ-glutamyl-glutamic acid establishing the highest number of significant correlations with the sensory attributes evaluated. Taken together, this work highlights the importance and validity of untargeted metabolomics coupled with sensory analysis to investigate the comprehensive chemical and sensory changes to dry meat during ripening.
Keyphrases
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  • mass spectrometry
  • machine learning
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