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Forty-three samples of green and black teas were analyzed by an electronic tongue technique. A class of metallic oxide-modified nickel foam electrodes (SnO 2 , ZnO, TiO 2 , Bi 2 O 3 ) 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, Bi 2 O 3 -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.

An effective and practical method, based on automatic peak detection, Lorentzian fitting, and polynomial fitting, is developed for aligning spectra, which can significantly reduce or eliminate systematic differences between Raman spectrometers. In this work, Lorentzian fitting of the experimental spectra is performed to precisely locate the peak positions. Then the standardization procedure is illustrated on the spectra of a chemical standard measured on primary and secondary instruments, and the spectra can be successfully aligned to each other after shift correction. It is shown that the similarity of pharmaceutical spectra acquired on three Raman spectrometers is then considerably improved after removal of the Raman shift difference. The result shows that the similarity of the spectra between primary and secondary spectrometers increases dramatically from about 0.8 to 0.95 after spectra standardization. This means that the proposed standardization method can effectively reduce or eliminate systematical errors and enhance spectral compatibility across different instruments.

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