Electronic Tongue System for Water Sample Authentication: A Slantlet-Transform-Based Approach

This paper proposes the development of a new approach for water sample authentication, in real life, using a pulse-voltametry-method-based electronic tongue instrumentation system. The system is developed as a parallel combination of several neural network classifiers, each dedicated to authenticate a specific category of water sample, and can be extended for more categories of water sample authentication. The system employs a slantlet-transform (ST)-based feature extraction module and two popular variants of neural networks for classification. The proposed system hybridizes ST with two variants of backpropagation-neural-network-based binary classifiers to develop an automated authentication tool. ST is regarded as an improved version of orthogonal discrete wavelet transform that can provide improved time localization with simultaneous achievement of shorter supports for the filters. This proposed system, implemented in a laboratory environment for various water samples available in India, showed encouraging average authentication percentage accuracy, on the order of over 80% for most water categories and even producing accuracy results exceeding 90%, for several categories.