title: Discrimination of Rice with Different Pretreatment Methods by Using a Voltammetric Electronic Tongue.
abstract: 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.
authors.affiliation: School of Electrical Engineering, Henan University of Technology, Zhengzhou 450007, China. hautwangli@haut.edu.cn.
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.affiliation: School of Electrical Engineering, Henan University of Technology, Zhengzhou 450007, China. niuqunfeng@haut.edu.cn.
authors.forename: Qunfeng
authors.initials: Q
authors.lastname: Niu
authors.affiliation: School of Electrical Engineering, Henan University of Technology, Zhengzhou 450007, China. huiyb@haut.edu.cn.
authors.forename: Yanbo
authors.initials: Y
authors.lastname: Hui
authors.affiliation: School of Food Science and Engineering, Henan University of Technology, Zhengzhou 450007, China. jinhuali@haut.edu.cn.
authors.forename: Huali
authors.initials: H
authors.lastname: Jin
mesh_terms: D016002:Discriminant Analysis; D004560:Electricity; D055664:Electrochemical Techniques / Q000295:instrumentation*; D004566:Electrodes; D004581:Electronics / Q000295:instrumentation*; D005163:Factor Analysis, Statistical; D005583:Fourier Analysis; D016571:Neural Networks, Computer; D012275:Oryza / Q000737:chemistry*; D012996:Solutions; D013997:Time Factors; D014059:Tongue
chemical_list: D012996:Solutions
keywords: Discriminant Factorial Analysis; cyclic voltammetry; discrimination; fast Fourier transform; radial basis function neural network; rice; variety prediction; voltammetric electronic tongue