Electronic nose for black tea quality evaluation by an incremental RBF network

Black Tea is conventionally tested by human sensory panel called “Tea Tasters”, who assign quality scores to different teas. Electronic nose-based studies aimed at co-relation of sensor array output signature with Tea Tasters’ scores have already been accomplished by the authors with good accuracy [N. Bhattacharyya, R. Bandyopadhyay, M. Bhuyan, B. Tudu, D. Ghosh, A. Jana, Electronic nose for black tea classification and correlation of measurements with “Tea Taster” marks, IEEE Trans. Instrum. Meas. 57 (2008) 1313–1321], where a number of conventional neural network topologies have been explored. One of the principal problems encountered in the above studies is collection of tea samples as tea industries in India are spread over dispersed locations and quality of tea varies considerably on agro-climatic condition, type of plantation, season of flush and method of manufacturing. As a result, the entire dataset is not available at a time and training the conventional neural network models becomes difficult. In this regard, classifiers having the incremental learning ability can be of great benefit by automatically including the newly presented patterns in the training dataset without affecting class integrity of the previously trained system. In the presented paper, the radial basis function (RBF) neural network with the incremental learning feature is used in the pattern recognition algorithm for black tea aroma classification with electronic nose.