Prediction of Collision Cross Section Values: Application to Non-Intentionally Added Substance Identification in Food Contact Materials.

Song, Xue-Chao; Dreolin, Nicola; Damiani, Tito; Canellas, Elena; Nerin, Cristina

Song, Xue-Chao; Dreolin, Nicola; Damiani, Tito; Canellas, Elena; Nerin, Cristina.

Afiliación

Song XC; Department of Analytical Chemistry, Aragon Institute of Engineering Research I3A, CPS-University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain.
Dreolin N; Waters Corporation, Altrincham Road, SK9 4AX Wilmslow, U.K.
Damiani T; Institute of Organic Chemistry and Biochemistry, Flemingovo námestí 542/2, 160 00 Prague, Czech Republic.
Canellas E; Department of Analytical Chemistry, Aragon Institute of Engineering Research I3A, CPS-University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain.
Nerin C; Department of Analytical Chemistry, Aragon Institute of Engineering Research I3A, CPS-University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain.

J Agric Food Chem ; 70(4): 1272-1281, 2022 Feb 02.

Article en En | MEDLINE | ID: mdl-35041428

Asunto(s)

Espectrometría de Movilidad Iónica; Aprendizaje Automático; Humanos

Palabras clave

NIAS; collision cross section; food contact materials; ion mobility; machine learning

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático / Espectrometría de Movilidad Iónica Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: J Agric Food Chem Año: 2022 Tipo del documento: Article País de afiliación: España Pais de publicación: Estados Unidos

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