Virtual Instrumentation Based Voltammetric Electronic Tongue for Classification of Black Tea

In this paper a virtual instrumentation based electronic tongue has been described by applying the principles of cyclic voltammetry. The set up consists of a three electrode system. A triangular pulse was applied as the input from a data acquisition card via an amplification and level shifter circuit and the voltage equivalent of the output current from the working solution was considered for data analysis. Initially, the huge amount of data has been compressed using discrete wavelet transform (DWT). Principal component analysis (PCA) and linear discriminant analysis (LDA) have been performed on the compressed data. Further, different pattern recognition models based on neural networks were used to carry out a correlation study with the tea tasters' score of 4 different grades of black tea. With unknown tea samples, encouraging results have been obtained with more than 90% classification rate.