Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing

TLDR

Chemometrics, multivariate statistical techniques from analytical chemistry, have become widely used in food science to handle large, complex datasets for tasks such as authenticating geographic origin, farming systems, and detecting adulteration. The article aims to give a practical overview of key chemometric tools for studying how processing variables affect food composition and for authenticating foods using chemical markers. The authors review pattern‑recognition techniques such as PCA, cluster analysis, and supervised multivariate methods for linking bioactive component levels to functional properties and for food authentication. The review presents practical examples and discusses the advantages and disadvantages of common chemometric tools to guide users in selecting suitable statistical approaches for complex, multivariate data.

Abstract

Abstract In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value‐added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real‐life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high‐dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data.