LDA-based vapor recognition using image-formed array sensor response for portable electronic nose

Abstract

The efficient formulation of measured data in artificial olfactory system is very important for simplicity, robustness and implementation of the algorithm especially in portable system which has limited resources. In this study, we applied the linear discriminant analysis (LDA) to E-nose measurements formulated in 2 dimensional matrices. The 160 measurements for 8 different vapors using 6 channel sensor array were identified with the accuracy of 98.75%. LDA is one of the most efficient computer vision algorithms to recognize image objects. It maintained the significant features for vapor classification during dimension reduction. This can simplify further processing like storage or transmission. Therefore, the proposed method will help the realization of the ubiquitous or embedded olfactory sensing system.