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Sensors 2009, 9(3), 1678-1691; doi:10.3390/s90301678

Least Square Regression Method for Estimating Gas Concentration in an Electronic Nose System

Dipartimento di Elettronica Informatica e Sistemistica, Università della Calabria, 87036 Rende (CS), Italy
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Received: 14 December 2008 / Revised: 7 March 2009 / Accepted: 10 March 2009 / Published: 10 March 2009
(This article belongs to the Section Chemical Sensors)
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Abstract

We describe an Electronic Nose (ENose) system which is able to identify the type of analyte and to estimate its concentration. The system consists of seven sensors, five of them being gas sensors (supplied with different heater voltage values), the remainder being a temperature and a humidity sensor, respectively. To identify a new analyte sample and then to estimate its concentration, we use both some machine learning techniques and the least square regression principle. In fact, we apply two different training models; the first one is based on the Support Vector Machine (SVM) approach and is aimed at teaching the system how to discriminate among different gases, while the second one uses the least squares regression approach to predict the concentration of each type of analyte. View Full-Text
Keywords: Electronic Nose; Support Vector Machine; Least Square Regression; Classification; Concentration Estimation Electronic Nose; Support Vector Machine; Least Square Regression; Classification; Concentration Estimation
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MDPI and ACS Style

Khalaf, W.; Pace, C.; Gaudioso, M. Least Square Regression Method for Estimating Gas Concentration in an Electronic Nose System. Sensors 2009, 9, 1678-1691.

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