Abstract
Semiconductor gas sensors have been widely applied to Volatile Organic Compounds (VOCs) detection. However, the poor selectivity in the actual complex gas environment has become a bottleneck to restrict the development of semiconductor gas sensors. In order to solve this problem, SnO2 gas sensor is prepared and temperature modulation measurement is carried out. In this paper, the method of zigzag-rectangular wave temperature modulation is first proposed, which improved the selectivity of the sensor and significantly increased the characteristic peak. Support vector machine (SVM) is used for pattern recognition, which proved the superior of the SVM in small sample size. The results demonstrate that the combination of zigzag-rectangular wave temperature modulation and SVM pattern recognition method can effectively improve the selectivity of the gas sensor.
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