TY - JOUR
T1 - Trends in Chemometrics
T2 - Food Authentication, Microbiology, and Effects of Processing
AU - Granato, Daniel
AU - Putnik, Predrag
AU - Kovačević, Danijela Bursać
AU - Santos, Jânio Sousa
AU - Calado, Verônica
AU - Rocha, Ramon Silva
AU - Cruz, Adriano Gomes Da
AU - Jarvis, Basil
AU - Rodionova, Oxana Ye
AU - Pomerantsev, Alexey
N1 - Publisher Copyright:
© 2018 Institute of Food Technologists®
PY - 2018/5
Y1 - 2018/5
N2 - 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.
AB - 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.
KW - classification
KW - food authentication
KW - multivariate statistical techniques
KW - one-class classifiers
KW - pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=85044662221&partnerID=8YFLogxK
U2 - 10.1111/1541-4337.12341
DO - 10.1111/1541-4337.12341
M3 - Article
AN - SCOPUS:85044662221
SN - 1541-4337
VL - 17
SP - 663
EP - 677
JO - Comprehensive Reviews in Food Science and Food Safety
JF - Comprehensive Reviews in Food Science and Food Safety
IS - 3
ER -