Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices.

Calle, José Luis P; Vázquez-Espinosa, Mercedes; Barea-Sepúlveda, Marta; Ruiz-Rodríguez, Ana; Ferreiro-González, Marta; Palma, Miguel

Calle, José Luis P; Vázquez-Espinosa, Mercedes; Barea-Sepúlveda, Marta; Ruiz-Rodríguez, Ana; Ferreiro-González, Marta; Palma, Miguel.

Afiliação

Calle JLP; Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, IVAGRO, ceiA3, Puerto Real, 11510 Cadiz, Spain.
Vázquez-Espinosa M; Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, IVAGRO, ceiA3, Puerto Real, 11510 Cadiz, Spain.
Barea-Sepúlveda M; Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, IVAGRO, ceiA3, Puerto Real, 11510 Cadiz, Spain.
Ruiz-Rodríguez A; Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, IVAGRO, ceiA3, Puerto Real, 11510 Cadiz, Spain.
Ferreiro-González M; Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, IVAGRO, ceiA3, Puerto Real, 11510 Cadiz, Spain.
Palma M; Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, IVAGRO, ceiA3, Puerto Real, 11510 Cadiz, Spain.

Foods ; 12(13)2023 Jun 29.

Article em En | MEDLINE | ID: mdl-37444273

Palavras-chave

BoxBehnken; fruit juices; ion mobility spectrometry; machine learning; optimization; random forest; support vector machine

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Foods Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Espanha

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