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

Published

Author(s)

Werickson Fortunato de Carvalho Rocha, Charles Prado, Niksa Blonder

Abstract

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.
Citation
Molecules

Keywords

food analysis, chemometrics, non-linear methods, artificial neural networks (ANN), self- organizing maps (SOM), support vector machine (SVM)
Created July 2, 2020