化学计量学
成熟
食品科学
干酪成熟
线性判别分析
代谢组学
化学
随机森林
分类器(UML)
数学
生物技术
人工智能
模式识别(心理学)
计算机科学
生物
色谱法
作者
Pier Paolo Becchi,Francisco J. Barba,Pascual García-Pérez,Sara Michelini,Valentina Pizzamiglio,Luigi Lucini
标识
DOI:10.1016/j.foodchem.2024.138938
摘要
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.
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