A feature extraction method for chemical sensors in electronic noses

We propose a new feature extraction method for use with chemical sensors. It is based on fitting a parametric analytic model of the sensor’s response over time to the measured signal, and taking the set of best-fitting parameters as the features. The process of finding the features is fast and robust, and the resulting set of features is shown to significantly enhance the performance of subsequent classification algorithms. Moreover, the model that we have developed fits equally well to sensors of different technologies and embeddings, suggesting its applicability to a diverse repertoire of sensors and analytic devices.