Discrimination of black tea using electronic nose and electronic tongue: A Bayesian classifier approach

Abstract

Electronic nose and electronic tongue is highly acceptable in the field of food quality research as well as in different food industry which are capable of analyzing food quality like human panel taster in a more accurate way. Just like human sensing system electronic nose can discriminate food samples based on aroma and electronic tongue classifies samples based on their taste. As per human perception process to perceive the taste of food the sense of smell is equally responsible to its taste. Considering this issue, we propose a multi sensor data fusion based on Bayesian theorem which is applied to the data obtained from electronic nose and electronic tongue for classification of black tea. Numerical results show that the error in classification is reduced considerably in multivariate data fusion compared to univariate case.