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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, anddoi:10.3390/molecules25133025 pmid:32630676 pmcid:PMC7411792 fatcat:pkourlj2v5anfl7zsgu2wa3nuy