A Neuro-Fuzzy Classifier-Cum-Quantifier for Analysis of Alcohols and Alcoholic Beverages Using Responses of Thick-Film Tin Oxide Gas Sensor Array

Abstract

A novel neuro-fuzzy classifier-cum-quantifier is presented. The proposed classifier retrieves both qualitative and quantitative information simultaneously from the steady-state responses of thick-film tin oxide gas sensor array when it was exposed to seven different kinds of alcohols and alcoholic beverages. The individual concentration bands were represented in the output feature space by fuzzy subsethood measure. The qualitative and quantitative classifications were done by training an artificial neural network (ANN) with backpropagation algorithm. Each output neuron of the network represented one out of the seven alcohols and alcoholic beverage classes and was trained to fire at the fuzzy subsethood value of the particular concentration band of a particular alcohol or alcoholic beverage whose sample was presented to the network. The proposed network gave satisfactory performance and simultaneous qualitative and quantitative classification of the alcohols and alcoholic beverages was obtained using a single neural network.