A feature extraction algorithm for multi-peak signals in electronic noses

The Lorentzian model is a powerful feature extraction technique for electronic noses. In a previous work, it was applied to single-peak transient signals and was shown to achieve lower classification error rate than other feature extraction techniques. Here, we generalize the Lorentzian model by showing how to apply it to transient signals that are comprised of more than a single peak. The model is based on a fast and robust fitting of the measured signals to a physically meaningful analytic curve. We show that this model fits equally well to sensors of different technologies and embeddings, suggesting its applicability to a diverse repertoire of sensors and analytic devices.