Instrumental testing of tea by combining the responses of electronic nose and tongue

In the tea industry, experienced tea tasters are employed for evaluation of tea quality and gradation of tea is done on the basis of their scores. This subjective method of assessment has numerous problems like inaccuracy and non-repeatability. Electronic nose and electronic tongue systems are recently being used for measurement of odor and taste of tea samples. As the senses of smell and taste are not independent, and both are interacting, the measured data from the individual systems are combined in this paper for improved estimation of black tea quality. It is found that for the combined system, both the clustering and classification rates improve when compared to the individual systems. With radial basis function neural network, the classification rate increases up to 93%, whereas with the independent systems, the classification rate obtained is 83–84% with electronic nose and 85–86% with electronic tongue.