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Internet Electronic Journal of Molecular Design - IEJMD, ISSN 1538-6414, CODEN IEJMAT
ABSTRACT - Internet Electron. J. Mol. Des. May 2003, Volume 2, Number 5, 348-357

Support Vector Machines Classification of Black and Green Teas Based on Their Metal Content
Ovidiu Ivanciuc
Internet Electron. J. Mol. Des. 2003, 2, 348-357

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Abstract:
Green and black teas are made from the processed leaves of Camellia sinensis. The metal content (Zn, Mn, Mg, Cu, Al, Ca, Ba, and K) of commercial tea samples, determined by inductively coupled plasma atomic emission spectroscopy, can be used in pattern recognition models to discriminate between the two tea types. We have investigated the application of SVM (support vector machines) for the classification of 44 tea samples (26 black tea and 18 green tea) based on the metal content. An efficient algorithm was tested for the selection of input parameters for the SVM models, in order to find the minimum metal profile that provides a good separation of the two classes. Using the hierarchical descriptor selection procedure, the initial group of eight metals was reduced to a set of three metals, namely Al, Ba, and K. The classification of the green and black teas was done with the dot, polynomial, radial basis function, neural, and anova kernels. The calibration and leave-20%-out cross-validation results show that the statistical performances of SVM models depend strongly on input descriptors, kernel type and various parameters that control the kernel shape. Several SVM models obtained with the anova kernel offered the best results, all with no error in calibration and one error in prediction (for a green tea sample). The hierarchical descriptor selection algorithm is an effective procedure to identify the optimum set of input variables for an SVM model. Using the Al, Ba, and K content determined with the inductively coupled plasma atomic emission spectroscopy, a highly predictive SVM model was developed for the classification of green and black teas.

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