Discrimination of green tea using an Epigallocatechin-3-gallate (EGCG) sensitive molecular imprinted polymer (MIP) based electrode

In this work, a simple approach of discriminating green tea samples has been proposed using an epigallocatechin-3-gallate (EGCG) sensitive molecular imprinted polymer (MIP) electrode modified with chemically synthesized nickel hydroxide (Ni(OH)2) nanoparticles. The nanoparticles were characterized by powder X-ray diffraction techniques (XRD) and the removal of the template molecule has been ascertained by UV-vis spectroscopy. A three electrode system has been employed to study the electrochemical characteristics of the electrode by means cyclic voltammetry (CV) and differential pulse voltammetry (DPV). Four different kinds of preprocessing techniques, namely – (i) Baseline subtraction, (ii) Autoscale, (iii) Relative scale 1 and (iv) Relative scale 2 were applied on the obtained data set and the best preprocessing technique was optimized. Further, principal component analysis (PCA) and linear discriminant analysis (LDA) were implemented on the preprocessed data set so as to observe the discrimination ability of the electrode on the basis of EGCG content in green tea. The separability index (SI) values for both PCA and LDA plots is calculated and it is observed that baseline subtraction provided the best result with a SI value of 8.72 and 16.01, respectively

Electronic nose for on-line quality evaluation of black tea using incremental SOM techniques

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