4.7 Article

Application of the voltammetric electronic tongue based on nanocomposite modified electrodes for identifying rice wines of different geographical origins

期刊

ANALYTICA CHIMICA ACTA
卷 1050, 期 -, 页码 60-70

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.aca.2018.11.016

关键词

Nanocomposites modified electrodes; Conducting polymer; Multi - walled carbon nanotubes; Rice wine; Pattern recognition

资金

  1. National Natural Science Foundation of China [31570005, 31560477]

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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. (C) 2018 Elsevier B.V. All rights reserved.

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