The limitations of the classical pattern recognition algorithms may be addressed by an incremental way of learning, through which the existing knowledge base can be expanded from the information gathered solely from new set of samples. In this study, a novel incremental Self Organizing Map (i-SOM) algorithm is proposed and applied on the data generated from an electronic nose for black tea quality evaluation. The algorithm enables data with similar features (data points corresponding to different batches of black tea having similar aroma content) to be clustered together without the necessity of access to previously generated dataset
