Automation for tea quality estimation is a challenging task and with the advent of electronic nose and electronic tongue systems this problem becomes quite addressable by instrumental means. Electronic nose judges tea sample based on aroma of the sample and based on taste tea quality can be classified using electronic tongue. For estimation of flavour of tea rather the overall quality of tea can be estimated if these two sensory responses can be fused. In this work, we have attempted to fuse these two sensory features using fuzzy fusion technique. A general fuzzy rule base is developed from the transient datasets obtained from electronic nose and tongue separately. The fuzzy system model can give accurate prediction with much simpler model than neural network. But both the system has certain advantages. In order to develop better classifier fuzzy neural network (FNN) model is also developed. Moreover the model works with transient responses and no data compression technique is employed. It is found that the combined sensor signature regarding tea quality estimation is quiet improved compared to individual sensor systems for all three classifiers and among these FNN is the best suited model for tea classification.

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The human perception process related to quality evaluation of food or beverages can be broadly divided into two processessensation and perception. While the process of sensation is responsible for collection of huge amount of data using the different sensory organs, the perception process interprets the data by a fusion process in the brain. In this paper, we describe a fusion model to combine the senses of smell and taste for quality assessment of black tea using two instruments -electronic nose and electronic tongue. As a first level of analysis, principal component of analysis is used for clustering and fuzzy ART artificial neural network for classification of the samples.

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