Feature Fusion for Prediction of Theaflavin and Thearubigin in Tea Using Electronic Tongue

Abstract

Liquor characteristics of black cut, tear, and curl tea mostly depend on two biochemical components like theaflavin (TF) and thearubigin (TR). Evaluation of tea quality can be done efficiently by estimating the concentration of TF and TR without using biochemical tests as it takes much time, which requires laborious effort for sample preparation, storage, and measurement. Moreover, the required instruments for this test are very costly. In this paper, we have proposed an efficient method of TF and TR prediction in a given tea sample using electronic tongue (ET) signal. Combinations of transformed features, like discrete cosine transform, Stockwell transform (ST), and singular value decomposition, of ET signals are fused to develop regression models to predict the contents of TF, TR, and TR/TF. Three different regression models such as artificial neural network, vector-valued regularized kernel function approximation, and support vector regression are used to evaluate the performance of the proposed method. High prediction accuracy using fusion of features ensures the effectiveness of the proposed method for prediction of TF and TR using ET signals.