Discrimination of Rice with Different Pretreatment Methods by Using a Voltammetric Electronic Tongue

Sensors

Li Wang¹  Qunfeng Niu¹  Yanbo Hui¹  Huali Jin² 

[1] School of Electrical Engineering, Henan University of Technology, Zhengzhou 450007, China; E-Mails:;School of Food Science and Engineering, Henan University of Technology, Zhengzhou 450007, China; E-Mail:

关键词: rice;    discrimination;    variety prediction;    voltammetric electronic tongue;    cyclic voltammetry;    fast Fourier transform;    Discriminant Factorial Analysis;    radial basis function neural network;   

DOI  :  10.3390/s150717767

来源: mdpi

【 摘 要 】

In this study, an application of a voltammetric electronic tongue for discrimination and prediction of different varieties of rice was investigated. Different pretreatment methods were selected, which were subsequently used for the discrimination of different varieties of rice and prediction of unknown rice samples. To this aim, a voltammetric array of sensors based on metallic electrodes was used as the sensing part. The different samples were analyzed by cyclic voltammetry with two sample-pretreatment methods. Discriminant Factorial Analysis was used to visualize the different categories of rice samples; however, radial basis function (RBF) artificial neural network with leave-one-out cross-validation method was employed for prediction modeling. The collected signal data were first compressed employing fast Fourier transform (FFT) and then significant features were extracted from the voltammetric signals. The experimental results indicated that the sample solutions obtained by the non-crushed pretreatment method could efficiently meet the effect of discrimination and recognition. The satisfactory prediction results of voltammetric electronic tongue based on RBF artificial neural network were obtained with less than five-fold dilution of the sample solution. The main objective of this study was to develop primary research on the application of an electronic tongue system for the discrimination and prediction of solid foods and provide an objective assessment tool for the food industry.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.