Improvement of capability for classifying odors 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. Quartz crystal microbalance (QCM) sensors are used to measure the sensor responses in this research. Our proposed method employs a short-time Fourier transform (STFT) algorithm and a stepwise discriminant analysis for feature extraction and dimensional reduction. The STFT is not only simple and easy to understand, but gives frequency characteristics with temporal information required for a real-time application. The variable selection due to the stepwise method also reduces the dimensions of pattern vectors that lead stable classification results. Finally, using a learning vector quantization (LVQ) method to evaluate the classification performance, we successfully achieved a high classification rate even if the odor concentration changed into various conditions whereas, the classification rate was insufficient in the case of using only magnitudes of sensor responses. This shows the robustness of the method against dynamical change of odor concentration.