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Application of the voltammetric electronic tongue based on nanocomposite modified electrodes for identifying rice wines of different geographical origins.
Wang, Jun; Zhu, Luyi; Zhang, Weilin; Wei, Zhenbo.
Afiliación
  • Wang J; Department of Biosystems Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, PR China.
  • Zhu L; Department of Biosystems Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, PR China.
  • Zhang W; Department of Biosystems Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, PR China.
  • Wei Z; Department of Biosystems Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, PR China. Electronic address: weizhb@zju.edu.cn.
Anal Chim Acta ; 1050: 60-70, 2019 Mar 07.
Article en En | MEDLINE | ID: mdl-30661592
In the study, the voltammetric electronic tongue based on three nanocomposites modified electrodes was applied for the identification of rice wines of different geographical origins. The nanocomposites were prepared by gold and copper nanoparticles in the presence of conducting polymers (polymer sulfanilic acid, polymer glutamic acid) and carboxylic multi - walled carbon nanotubes. The modified electrodes showed high sensitivity to guanosine - 5' - monophosphate disodium salt, tyrosine and gallic acid which have good correlation with the geographical origins of rice wines. Scanning electron microscopy was performed to display the surface morphologies of the nanocomposites, and cyclic voltammetry was applied to study the electrochemical behaviors of the taste substances on the electrode surfaces. Four types of electrochemical parameters (pH, scan rates, accumulation potentials and time) were optimized for getting a low limit of the detection of each taste substance. The geographical information of rice wines was obtained by the modified electrodes based on two types of multi - frequency large amplitude pulse voltammetry, and "area method" was applied for extracting the feature data from the original information obtained. Based on the area feature data, principal component analysis, locality preserving projection (LPP), and linear discriminant analysis were applied for the classification of the rice wines of different geographical origins, and LPP presented the best results; extreme learning machine (ELM) and alibrary for support vector machines were applied for predicting the geographical origins of rice wines, and ELM performed better.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Oryza / Vino / Nanocompuestos / Técnicas Electroquímicas / Nariz Electrónica / Geografía Tipo de estudio: Prognostic_studies Idioma: En Revista: Anal Chim Acta Año: 2019 Tipo del documento: Article Pais de publicación: Países Bajos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Oryza / Vino / Nanocompuestos / Técnicas Electroquímicas / Nariz Electrónica / Geografía Tipo de estudio: Prognostic_studies Idioma: En Revista: Anal Chim Acta Año: 2019 Tipo del documento: Article Pais de publicación: Países Bajos