Study of Odor Classification in Dynamically Changing Concentration using QCM Sensor Array and Short-Time Fourier Transform

In this paper, we propose a method for improving the capability of odor classification in dynamical change of concentration often encountered in the ambient air. Our method employs a short-time Fourier transform (STFT) algorithm and a stepwise discriminant analysis for feature extraction and dimensional reduction. Finally, using learning vector quantization (LVQ) method to evaluate the classification performance, we successfully achieved high classification rate even if the odor concentration changes irregularly at different humidity levels whereas the classification rate was insufficient in the case of using only magnitudes of sensor responses.