Classification of different varieties of Oolong tea using novel artificial sensing tools and data fusion

Abstract

An improved classification of Oolong tea with different varieties is presented combining two novel artificial sensing tools (i.e. gustatory sensors and olfactory sensors). Herein, the gustatory sensors system was developed by using four electrodes (gold, copper, platinum and glassy carbon) in a standard three-electrode configuration, and the olfactory sensors system was developed based on a colorimetric sensors array. Initially, the data obtained from the two sensor systems was analyzed separately. Then, the potential of the combination of two sensors systems for classification was investigated. Principal component analysis (PCA) and linear discriminant analysis (LDA), as two classification tools, were used for data classification. The results show that the discrimination capability of the combined system is superior to that obtained with the two sensors systems separately, and eventually LDA achieved 100% classification rate by cross-validation. This work indicates that the combination of gustatory sensors system and olfactory sensors system can be a useful tool for the classification of Oolong tea with different varieties.