Fuzzy Logic-Based Classification and Authentication of Beverages

Abstract

This article aims at designing a fuzzy logic-based classification and authentication system for commercially available tea and water brands. Electronic tongue instrumentation-based experimentally collected data had been used to design the classification and authentication system. Tea and water samples of six different types were evaluated using pulse voltammetric technique. However, this data generation and reporting are not under the scope of the current study. The classifier/authenticator design is a three-step procedure. At first, identification of the data in subspace was done using Sammon's non-linear mapping technique followed by entropy-based fuzzy clustering of water/tea data set formed and, finally, designing the expert system optimized using a particle swarm optimization to authenticate unknown water and tea samples with 100% efficiency. Thus, it deals with dimensionality reduction of the data set for visualization, clustering based on similarity, and development of a fuzzy logic-based expert system, as it is a powerful tool for dealing with imprecision and uncertainty.