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 sho...

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Bibliographic Details
Published in:Sensors and actuators. B, Chemical Chemical, 2007-01, Vol.120 (2), p.467-472
Main Authors: Haddad, R., Carmel, L., Harel, D.
Format: Article
Language:English
Subjects:
Actuators
Algorithms
Classification
Electronic nose
Electronic noses
Feature extraction
Fittings
Mathematical analysis
Multiple peaks
Sensors
Signal processing
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Description
Summary: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.
ISSN:0925-4005
1873-3077
DOI:10.1016/j.snb.2006.02.048