Design and Implementation of an Electronic Nose Based on Power-Law Model

Abstract

Electronic noses are intelligent systems having the ability to identify and quantify different gases. They are capable to handle large data sets and to perform quite complex signal processing by employing commercial sensors. In this paper we examine four different gases (ethanol, methanol, acetone and benzene). The data are acquired and processed by a prototype multi-sensor monitoring system (electronic nose) which has been designed at University of Calabria. It includes five gas sensors plus two auxiliary sensors, for temperature and humidity, respectively. The system is equipped by an appropriate interface to a PC for visualizing and analyzing the data.
We introduce an analytical power law model of sensor response. Least square regression is performed to estimate model parameters. The model is then used for both gas classification and concentration estimate. The results of the numerical experiments on 172 gas samples are reported.