On the Use of the SVM Approach in Analyzing an Electronic Nose

Abstract

We present an Electronic Nose (ENose) which is aimed both at identifying the type of gas and at estimating its concentration. Our system contains 8 sensors, 5 of them being gas sensors (of the class TGS from FIGARO USA, INC., whose sensing element is a tin dioxide (SnOz) semiconductor), the remaining being a temperature sensor (LM35 from National Semiconductor Corporation), a humidity sensor (HIH-3610 from Honeywell), and a pressure sensor (XFAM from Fujikura Ltd.). Our integrated hardware-software system uses some machine learning principles and least square regression principle to identify at first a new gas sample, and then to estimate its concentration, respectively. In particular we adopt a training model using the Support Vector Machine (SVM) approach to teach the system how discriminate among different gases, then we apply another training model using the least square regression, for each type of gas, to predict its concentration.