Surface acoustic wave E-nose sensor based pattern generation and recognition of toxic gases using artificial neural network techniques

Abstract

SAW E-nose sensor is one of the chemical sensor detectors to sense and detect the toxic vapors or gases. This paper proposes an approach to process the SAW sensor data and to predict the type of chemical warfare agents (CWA). Artificial neural network (ANN) approach is one of the pattern recognition technique for processing the signal produced by SAW E-nose. We have applied Principal component analysis (PCA) technique to normalize the data sets of SAW sensors. Here, we have designed a system to predict the toxic vapors like ammonia, acetone, ethylene, and ethanol. This pattern recognition system also classifies the humidity of the toxic vapors. Sensor arrays were used for predicting different types of toxic vapor as a result. The results were obtained through MATLAB tool with sensor data set converted from analog to digital data type.