Longjing tea quality classification by fusion of features collected from E-nose

Abstract

Electronic nose (E-nose) was employed for quality classification of Xihu-Longjing tea in this paper. Four grades of Xihu-Longjing tea, which were classified by tea experts, were attempted in the experiment. The aim of this study is to conduct feature fusion method with two kinds of features — time domain features and frequency domain features. Accounting for the redundant information provided by some sensors, Fisher criterion was conducted for sensor selection, and dimensionality reduction algorithms were applied for further feature selection. Experimental results showed that the fused features could better represent signal characteristics compared with the single features. Based on the fused features, the performances of linear and nonlinear dimensionality reduction algorithms were compared. Experimental results indicated that nonlinear algorithms were more effective in feature selection than linear algorithms, and the highest recognition rate could reach to 100% by KLDA. The results achieved in this paper showed the superiority of the fused features in representing signal characteristics, and indicated that E-nose can be successfully used in quality classification of Xihu-Longjing tea.