Analysis and classification of coffee beans using single coffee bean mass spectrometry with machine learning strategy.

Tsai, Jia-Jen; Chang, Che-Chia; Huang, De-Yi; Lin, Te-Sheng; Chen, Yu-Chie

Tsai, Jia-Jen; Chang, Che-Chia; Huang, De-Yi; Lin, Te-Sheng; Chen, Yu-Chie.

Afiliación

Tsai JJ; Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan.
Chang CC; Department of Applied Mathematics, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan.
Huang DY; Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan.
Lin TS; Department of Applied Mathematics, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan; National Center for Theoretical Sciences, National Taiwan University, Taipei 10617, Taiwan. Electronic address: tslin@math.nctu.edu.tw.
Chen YC; Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan; International College of Semiconductor Technology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan. Electronic address: yuchie@nycu.edu.tw.

Food Chem ; 426: 136610, 2023 Nov 15.

Article en En | MEDLINE | ID: mdl-37331144

Asunto(s)

Coffea; Animales; Coffea/química; Viverridae; Semillas/química; Espectrometría de Masas; Análisis Espectral

Palabras clave

Ambient ionization; Coffee; Machine learning; Mass Spectrometry; Neural network; Palm civet coffee

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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Coffea Idioma: En Revista: Food Chem Año: 2023 Tipo del documento: Article

Texto completo

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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Coffea Idioma: En Revista: Food Chem Año: 2023 Tipo del documento: Article