Abstract
For electronic nose systems, existing algorithms for single odor analysis have provided commercial-grade recognition results. However, for practical applications to deal with mixed odors, effective algorithms are yet to be developed. One of the problems with mixed-odor analysis using metal-oxide semiconductor sensors is the long saturation time (to reach steady-state) of sensor responses which are important features for odor analysis. In this thesis, we propose an efficient method to reduce the time to recognize mixed odors before the sensor responses reach saturation states. Our method consists of signal pre-processing, sensor response feature extraction, feature selection and normalization, and K-Nearest Neighbor (KNN) classification. Experiments show that the proposed method improves mixed-odor analysis time (using metal-oxide semiconductor sensors) significantly without sacrificing the accuracy of the recognition.