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Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods



Werickson Fortunato de Carvalho Rocha, Charles Prado, Niksa Blonder


Food analysis is a challenging analytical problem, often addressed using sophisticated laboratory methods that produce large data sets. Linear and non-linear multivariate methods can be used to process these types of datasets and to answer questions such as whether product origin is accurately labeled or whether a product is safe to eat. In this review we will present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and multi-layer artificial neural networks in the field of chemometrics related to food analysis. We will also discuss criteria to determine when non-linear methods are better suited for use instead of traditional chemometric methods. The principles of algorithms will be described, and several types of use cases of these methods will be presented in the application to solving the problems of exploratory analysis, classification, and regression. Non- linear methods will also be compared, in terms of advantages and disadvantages, with traditional chemometric approaches.


food analysis, chemometrics, non-linear methods, artificial neural networks (ANN), self- organizing maps (SOM), support vector machine (SVM)


Fortunato de Carvalho Rocha, W. , Prado, C. and Blonder, N. (2020), Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods, Molecules, [online], (Accessed June 25, 2024)


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Created July 1, 2020, Updated October 12, 2021