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

Werickson Fortunato de Carvalho Rocha, Charles Bezerra do Prado, Niksa Blonder
2020 Molecules  
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 present the application of non-linear methods such as artificial neural networks, support vector machines, self-organizing maps, and
more » ... yer artificial neural networks in the field of chemometrics related to food analysis. We discuss criteria to determine when non-linear methods are better suited for use instead of traditional methods. The principles of algorithms are described, and examples are presented for solving the problems of exploratory analysis, classification, and prediction.
doi:10.3390/molecules25133025 pmid:32630676 pmcid:PMC7411792 fatcat:pkourlj2v5anfl7zsgu2wa3nuy