Discrimination of green tea quality using the electronic nose technique and the human panel test, comparison of linear and nonlinear classification tools

Electronic nose (E-nose) technique was attempted to discriminate green tea quality instead of human panel test in this work. Four grades of green tea, which were classified by the human panel test, were attempted in the experiment. First, the E-nose system with eight metal oxide semiconductors gas sensors array was developed for data acquisition; then, the characteristic variables were extracted from the responses of the sensors; next, the principal components (PCs), as the input of the discrimination model, were extracted by principal component analysis (PCA); finally, three different linear or nonlinear classification tools, which were K-nearest neighbors (KNN), artificial neural network (ANN) and support vector machine (SVM), were compared in developing the discrimination model. The number of PCs and other model parameters were optimized by cross-validation. Experimental results showed that the performance of SVM model was superior to other models. The optimum SVM model was achieved when 4 PCs were included. The back discrimination rate was equal to 100% in the training set, and predictive discrimination rate was equal to 95% in the prediction set, respectively. The overall results demonstrated that E-nose technique with SVM classification tool could be successfully used in discrimination of green tea's quality, and SVM algorithm shows its superiority in solution to classification of green tea's quality using E-nose data.