A Feature Extraction Method Using Linear Model Identification of Voltammetric Electronic Tongue

A novel technique of feature extraction for a voltammetric electronic tongue is presented using system identification method with the subsequent synthesis of an equivalent circuit for black tea and then to predict the total theaflavin (TF) content in it. The equivalent circuit parameters for different tea samples are estimated using the current response data obtained from the voltammetric electronic tongue, on which system identification procedure is applied. These identified circuit parameters are then treated as the features of tea samples. The efficacy of the features is corroborated by developing prediction models for TF and comparing the prediction results with reference to TF content in tea. Various regression models such as principal component regression, partial least-squares regression, independent component regression, multilayer feedforward neural network regression, support vector regression, and extreme learning machine (ELM)-based regression models have been evaluated. The proposed feature extraction method performs better when its prediction accuracy was compared with that of the discrete wavelet transform (DWT), a well-established feature extraction method and the neighborhood components analysis (NCA) for regression, and a feature selection method was introduced here for the first time for signal processing of electronic tongue. A significant reduction in the number of features has been obtained in this work over existing feature extraction techniques.