Classification of Tea Samples using Learning Vector Quantization Neural Network

Abstract

A supervised multi-class classification method based on learning vector quantization (LVQ) neural network was proposed to classify tea samples of five commercial brands; Brook bond, Double-Diamond, Lipton, Lipton-Darjeeling and Marvel. Data required for classifier design were obtained by performing laboratory experiments with electronic tongue. Multi-class classifiers based on multilayer perceptron, weighted k-nearest neighbors and Mahalanobis distance were developed to compare the results of LVQ neural network classifier. The LVQ neural network classifier showed superior performance with classification rate of 97.9%.