On the use of the transient information for gas identification using microelectronic gas sensor

Gas identification using microelectronic gas sensor represents a big challenge for pattern recognition systems due to a number of problems mainly related to non-selectivity and the drift. The use of preprocessing can often greatly improve the recognition performance. The aim of this paper is to evaluate the performance of different preprocessing techniques using transient information for gas identification from sensor array signals. We compare the classification accuracy of three feature extraction techniques based on steady state value, transient intergrals and dynamic slope. It was found that the transient information plays a critical role in improving the classification performance. In addition the proposed dynamic slope feature extraction technique is hardware friendly which makes the prospect of building a smart gas sensor very promising.