Vinegar Classification Based on Feature Extraction and Selection From Tin Oxide Gas Sensor Array Data

Abstract

Tin oxide gas sensor array based devices were often cited in publications dealing with food products. However, during the process of using a tin oxide gas sensor array to analysis and identify different gas, the most important and difficult was how to get useful parameters from the sensors and how to optimize the parameters. Which can make the sensor array can identify the gas rapidly and accuracy, and there was not a comfortable method. For this reason we developed a device which satisfied the gas sensor array act with the gas from vinegar. The parameters of the sensor act with gas were picked up after getting the whole acting process data. In order to assure whether the feature parameter was optimum or not, in this paper a new method called “distinguish index”(DI) has been proposed. Thus we can assure the feature parameter was useful in the later pattern recognition process. Principal component analysis (PCA) and artificial neural network (ANN) were used to combine the optimum feature parameters. Good separation among the gases with different vinegar is obtained using principal component analysis. The recognition probability of the ANN is 98 %. The new method can also be applied to other pattern recognition problems.