Abstract
this paper presents the performance of gas sensors as electronic nose coupled with pattern recognition method for gases identification. In fact, the implementation of the electronic nose in a characterization process is based on two fundamental phases: a learning phase and a phase of identification. That is why we need an accurate extraction method in order to obtain performant classification. In this study, we propose to extract transient parameters in a dynamic mode: derivate and integral. The performance of these features is validated by the analysis method: principal component analysis (PCA) and K nearest neighbors (KNN), which present 98, 74% rate classification.