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

Rocha, Werickson Fortunato de Carvalho; Prado, Charles Bezerra do; Blonder, Niksa

Rocha, Werickson Fortunato de Carvalho; Prado, Charles Bezerra do; Blonder, Niksa.

Afiliación

Rocha WFC; National Institute of Metrology, Quality and Technology (INMETRO), Av. N. S. das Graças, 50, Xerém, Duque de Caxias 25250-020, RJ, Brazil.
Prado CBD; National Institute of Standards and Technology (NIST), 100 Bureau Drive, Stop 8390, Gaithersburg, MD 20899, USA.
Blonder N; National Institute of Metrology, Quality and Technology (INMETRO), Av. N. S. das Graças, 50, Xerém, Duque de Caxias 25250-020, RJ, Brazil.

Molecules ; 25(13)2020 Jul 02.

Article en En | MEDLINE | ID: mdl-32630676

RESUMEN

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 multi-layer 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.

Asunto(s)

Quimioinformática/métodos; Análisis de los Alimentos/métodos; Algoritmos; Análisis de los Alimentos/estadística & datos numéricos; Redes Neurales de la Computación; Dinámicas no Lineales; Máquina de Vectores de Soporte

Palabras clave

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

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Análisis de los Alimentos / Quimioinformática Tipo de estudio: Prognostic_studies Idioma: En Revista: Molecules Asunto de la revista: BIOLOGIA Año: 2020 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Suiza

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