Optimization of an electronic nose for rapid quantitative recognition

This paper describes the optimization of an electronic nose (E-nose) equipped with metal-oxide gas sensors and dedicated to continuous concentration monitoring of volatile molecules. The optimization concerns particularly the selection of more appropriate characteristic features coupled with measurement conditions in order to minimize both the measurement time and the gas sensor drifts. First, a promising and fast feature corresponding to the maximum of the derivative curve of the sensor time-response is explored. The performance of this feature is demonstrated by comparison with a conventional steady-state feature, especially regarding its occurrence time, stability and sensitivity. Then the optimization of the measurement time (delay between two successive detections) has been illustrated and discussed. Optimized operating conditions and feature were finally validated by using non-supervised and supervised data mining analyses which show robust concentration discrimination. This optimization work constitutes an important step in real time applications for E-nose users.