Tea Quality Prediction by Autoregressive Modeling of Electronic Tongue Signals PROJECT TITLE : Tea Quality Prediction by Autoregressive Modeling of Electronic Tongue Signals ABSTRACT: In this paper, a completely unique technique to model the responses of electronic tongue (ET) sensors using autoregressive (AR) and AR moving average techniques is presented. The transient response of each electrode present within the sensor array of an ET is characterised with tea samples of different qualities. Models coefficients are used as the characteristics options of the ET response admire the tea samples. Three different classifiers, namely, artificial neural network, vector valued regularized kernel perform approximation, and one-versus-one support vector machine, are utilized to judge the performance of these features to discriminate the quality of black tea. Experimental results on three sorts of voltammetric measurement knowledge show that the proposed technique may be very useful for prediction of tea quality. The present model-primarily based classification technique is terribly simple and provides better or similar performance compared with another ways proposed within the literature for ET signal classification. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Support Vector Machines, Array Signal Processing Autoregressive Moving Average Processes Beverage Industry Neural Nets Wideband distributed coherent aperture radar based on stepped frequency signal: theory and experimental results
