Black tea classification employing feature fusion of E-Nose and E-Tongue responses

In this article, wavelet energy feature (WEF) has been extracted from the responses of e-nose and e-tongue for the classification of different grades of Indian black tea. For WEF, different decomposition levels of wavelet packet transform have been tested for both the systems and performance is evaluated with K- Nearest Neighbors classifier. Energy features of the best-suited decomposition level for e-nose and e-tongue have been calculated and fused to get a combined sensor response. Results confirm that the clustering nature (PCA plot) and classification accuracy (10-fold cross-validated based on KNN) have improved (accuracy 99.75%) with the applied method on the combined data. Moreover, this method is compared with some benchmark classification methods namely PLS-DA and Sammon's projection method which exhibits the superiority of the WEF extraction method combining the responses of multi sensory system.