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

Comprehensive Reviews in Food Science and Food Safety - Tập 17 Số 3 - Trang 663-677 - 2018
Daniel Granato1, Predrag Putnik2, Danijela Bursać Kovačević2, Jânio Sousa Santos1, Verônica Calado3, Ramon S. Rocha4, Adriano G. Cruz4, Basil Jarvis5, Oxana Ye. Rodionova6, Alexey L. Pomerantsev6
1Dept. of Food Engineering State Univ. of Ponta Grossa Av. Carlos Cavalcanti, 4748 84030–900 Ponta Grossa Brazil
2Faculty of Food Technology and Biotechnology Univ. of Zagreb Pierottijeva 6 10000 Zagreb Croatia
3School of Chemistry Federal Univ. of Rio de Janeiro Rio de Janeiro Brazil
4Dept. de Alimentos, Inst. Federal de Educação Ciência e Tecnologia (IFRJ) 20270‐021 Rio de Janeiro Brazil
5Dept. of Food and Nutrition Sciences, School of Chemistry, Food and Pharmacy The Univ. of Reading Whiteknights Reading Berkshire RG6 6AP U.K
6Semenov Inst. of Chemical Physics RAS Kosygin str. 4 119991 Moscow Russia

Tóm tắt

AbstractIn 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.

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