Abstract
Electronic nose is a system, which can determine the fingerprint of gas sample by a sensor array coupled to pattern recognition system. In this paper, a sensor array based on WO 3 gas sensor has been described, and a feature extraction technique, including integral and primary derivative, is reported, which leads to higher classification performance as compared to the classical features: fractional resistance change ARs and ARf . The sensor array has been exposed to ozone, ethanol, acetone, and a mixture of ozone and ethanol while keeping the temperature constant. The data array has been normalized and auto scaled then analyzed with the principal component analysis. Results indicate that successful classifications have been gotten in the discrimination of three kinds of oxidizing and reducing gas with the proposed feature extraction method using support vector machine which shows that 97.5% were correctly discriminated with integral and primary derivate comparing the classical variable with 85%. The results of data analysis implied that coupling the extracted features in the same database would be interesting and more robust then the standards features.