Signal Processing for Multi-sensor E-nose System: Acquisition and Classification

In this paper we review principle component analysis, linear discriminant analysis (LDA), k-nearest neighbor, feed forward backpropagation neural network, support vector machine, and radial basis function neural network (RBFNN) algorithms applied to electronic nose (E-Nose) for classification and detection. We show a method to extend the linear discriminant analysis (LDA) for multiclass (i.e. more than two class) LDA. By considering data alike typical E-Nose response we also show that RBFNN method need less time to classify new data. Thus RBFNN is more prominent in real time application for object identification from odor.