Classification of Green and Black Teas by PCA and SVM Analysis of Cyclic Voltammetric Signals from Metallic Oxide-Modified Electrode

Forty-three samples of green and black teas were analyzed by an electronic tongue technique. A class of metallic oxide-modified nickel foam electrodes (SnO2, ZnO, TiO2, Bi2O3) was compared in their sensitivity in this system. The signals obtained by cyclic voltammetry were submitted to multivariate data analysis. In the explorative analysis based on principal component analysis (PCA), the score plots showed that two of these sensors were able to distinguish varieties of teas. The resulting PCA scores were modeled with a support vector machine (SVM) that accomplished final prediction with the qualitative classification of teas. The optimal SVM model was achieved after grid search optimization of some parameters and the conduction of the three commonly used kernel functions. With a comparison of classification accuracies, Bi2O3-modified nickel foam electrode performed the best among the four electrodes and SVM model using the polynomial kernel attained the highest within the three used kernels. This work demonstrated that cyclic voltammetry combined with the SVM pattern recognition method could be successfully applied in the classification of green and black teas.