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

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.

[1]  N. Ancona,et al.  Support vector machines for olfactory signals recognition , 2003 .

[2]  Jacob Cohen,et al.  Applied multiple regression/correlation analysis for the behavioral sciences , 1979 .

[3]  Sang-Woo Ban,et al.  SnO/sub 2/ gas sensing array for combustible and explosive gas leakage recognition , 2002 .

[4]  Xiao-Dong Wang,et al.  Signals recognition of electronic nose based on support vector machines , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[5]  J. Gardner,et al.  An electronic nose system to diagnose illness , 2000 .

[6]  Julian W. Gardner,et al.  Sensors and Sensory Systems for an Electronic Nose , 1992 .

[7]  M. Pardo,et al.  Classification of electronic nose data with support vector machines , 2005 .

[8]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[9]  Stephen I. Gallant,et al.  Neural network learning and expert systems , 1993 .

[10]  Ida A. Casalinuovo,et al.  Application of Electronic Noses for Disease Diagnosis and Food Spoilage Detection , 2006, Sensors (Basel, Switzerland).

[11]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[12]  Jun Wang,et al.  Evaluation of peach quality indices using an electronic nose by MLR, QPST and BP network , 2008 .

[13]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[14]  A. K. Srivastava,et al.  Detection of volatile organic compounds (VOCs) using SnO2 gas-sensor array and artificial neural network , 2003 .

[15]  Gérard Dreyfus,et al.  Single-layer learning revisited: a stepwise procedure for building and training a neural network , 1989, NATO Neurocomputing.

[16]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[17]  Francesco Tortorella,et al.  Identification and quantification of individual volatile organic compounds in a binary mixture by SAW multisensor array and pattern recognition analysis , 2002 .

[18]  H. Troy Nagle,et al.  Handbook of Machine Olfaction: Electronic Nose Technology , 2003 .

[19]  Ricardo Gutierrez-Osuna,et al.  Pattern analysis for machine olfaction: a review , 2002 .

[20]  Gao Daqi,et al.  Simultaneous estimation of odor classes and concentrations using an electronic nose with function approximation model ensembles , 2007 .