Improvement of Robustness of Odor Classification in Dynamically Changing Concentration Against Environmental Change

Abstract

In this paper, we propose a method for improving the robustness of odor classification against humidity change when the odor concentration changes dynamically. We apply a short-time Fourier transform (STFT) to sensor responses to obtain the frequency characteristics, and then employ a stepwise discriminant analysis to select the frequency components effective for the odor classification. We improve the classification performance by selecting the components robust against humidity change and combining them with humidity data. Using a learning vector quantization (LVQ) method, we successfully achieved high classification rate even if the odor concentration changed dynamically and irregularly at different humidity levels whereas the classification rate was insufficient in the case of using only magnitudes of sensor responses.