4.7 Article

Multivariate data analysis strategy to monitor Trentingrana cheese real-scale production through volatile organic compounds profiling

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LWT-FOOD SCIENCE AND TECHNOLOGY
卷 173, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.lwt.2022.114364

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Grana cheese; Volatile organic compounds; Anova-simultaneous component analysis; Orthogonal partial least squares regression

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By analyzing two years of production data of Trentingrana cheese, it was found that the milk collection process has a significant impact on the content of organic acids, esters, and ketones, which are volatile organic compounds (VOCs) in the cheese.
Volatile organic compounds (VOCs) in cheese, as result of the chemical, physical and microbiological properties of the raw milk, are related to its sensory properties and consumer's acceptability. Measurement of VOCs can be related to the quality of the production process, highlighting changes in the raw materials or the process con-ditions. In the present study, we tested the suitability of ANOVA-Simultaneous Component Analysis (ASCA) to extract useful information from volatile organic compound data measured over two years of production of Trentingrana cheese in a real production context where several confounding factors are present.A total of 317 cheese wheels were collected from the 15 cooperative dairy factories every two months. The ASCA analysis indicates that the milk collection affects the VOC profiles. To deeper investigate this factor, an Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model was developed to estimate the asso-ciations between VOCs and process characteristics of the dairy factory. Results showed that the milk collection procedure affects the content of organic acids, esters, and ketones of the cheeses.

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