Statistical assessment of quantification methods used in gas sensor system

Abstract This paper presents comparative study of neural, fuzzy, fuzzy neural and support vector machines approach to gas sensor array responses processing. Case study is quantification of toluene concentration in the environment with variable humidity. Sensor array containing TGS 800 and TGS 2000 series devices was exposed to gas mixtures to provide data sets for training, testing and validation of algorithms. Series of algorithms were prepared and tested using software written in MATLAB environment (partially in-house developed). The algorithms performance was compared and classic statistical test by McNemar was used to check whether the differences were significant or not. It was shown that response to this question may depend on the required quality of the sensor system. The results of statistical tests may be used in several ways to support the choice of the most suitable data processing method.

[1]  T. Ross.  Fuzzy Logic with Engineering Applications , 1994 .

[2]  T. Sobanski,et al.  Analysis of gas mixture using sensor array and artificial neural networks , 2002 .

[3]  J. Roggen,et al.  The influence of relative humidity on the response of tin oxide gas sensors to carbon monoxide , 1991 .

[4]  Johan A. K. Suykens,et al.  LS-SVMlab Toolbox User's Guide , 2010 .

[5]  Fevzullah Temurtas,et al.  A study on quantitative classification of binary gas mixture using neural networks and adaptive neuro-fuzzy inference systems , 2006 .

[6]  K. Ihokura,et al.  The tin dioxide gas sensor , 1993 .

[7]  Przemyslaw M. Szecowka,et al.  Simultaneous quantification of carbon monoxide and methane in humid air using a sensor array and an artificial neural network , 1997 .

[8]  J. Brezmes,et al.  Qualitative and quantitative analysis of volatile organic compounds using transient and steady-state responses of a thick-film tin oxide gas sensor array , 1997 .

[9]  R. Todeschini,et al.  A chemometric approach based on a novel similarity/diversity measure for the characterisation and selection of electronic nose sensors. , 2006, Analytica chimica acta.

[10]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[11]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[12]  Andrzej Szczurek,et al.  The characteristics of a “stop–flow” mode of sensor array operation using data with the best classification performance , 2009 .

[13]  Seppo Leppävuori,et al.  Effect of water vapour on the CO response behaviour of tin dioxide sensors in constant temperature and temperature-pulsed modes of operation , 1990 .

[14]  D. S. Vlachos,et al.  Fuzzy neural networks for gas sensing , 1996 .

[15]  Q. Mcnemar.  Note on the sampling error of the difference between correlated proportions or percentages , 1947, Psychometrika.

[16]  Mancang Liu,et al.  Prediction of relative sensitivity of the olfactory and nasal trigeminal chemosensory systems for a series of the volatile organic compounds based on local lazy regression method , 2009 .

[17]  José Santos,et al.  Application of artificial neural networks to calculate the partial gas concentrations in a mixture , 2001 .

[18]  Hélène Debéda,et al.  Methane sensing : from sensitive thick films to a reliable selective device , 1997 .

[19]  Giorgio Sberveglieri,et al.  An electronic nose for the recognition of the vineyard of a red wine , 1996 .

[20]  L. G. Kwong,et al.  Data analysis for a hybrid sensor array , 2005 .

[21]  Ludovic Duponchel,et al.  Comparison of supervised pattern recognition methods with McNemar’s statistical test: Application to qualitative analysis of sugar beet by near-infrared spectroscopy , 2003 .

[22]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[23]  A. Szczurek,et al.  Application of a sensor system for determining the kind and quantity of two component VOC mixtures in air after the use of solvents. , 2009, Journal of environmental monitoring : JEM.

[24]  Takamichi Nakamoto,et al.  High speed active gas/odor sensing system using adaptive control theory , 1997 .

[25]  Gerhard Niebling.  Identification of gases with classical pattern-recognition methods and artificial neural networks , 1994 .

[26]  Fabrizio Davide,et al.  A composed neural network for the recognition of gas mixtures , 1995 .

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

[28]  T. Kijima,et al.  A new type of host compound consisting of α-zirconium phosphate and an animated cyclodextrin , 1986, Nature.

[29]  Fevzullah Temurtas,et al.  A study on radial basis function neural network size reduction for quantitative identification of individual gas concentrations in their gas mixtures , 2007 .

[30]  Pengyuan Yang,et al.  Quantitative fuzzy neural network for analytical determination , 2002 .

[31]  René Lalauze,et al.  A new approach to selective detection of gas by an SnO2 solid-state sensor , 1984 .

[32]  K. S. Nagaraja,et al.  Utilization of Sr(II)-added calcium aluminate for the detection of volatile organic compounds , 2007 .

[33]  Przemyslaw M. Szecowka,et al.  Application of sensor array and neural networks for quantification of organic solvent vapours in air , 1999 .

[34]  Wolfgang Göpel,et al.  Characterisation of food freshness with sensor arrays , 1994 .

[35]  Wojciech Maziarz,et al.  Dynamic response of a semiconductor gas sensor analysed with the help of fuzzy logic , 2003 .

[36]  Julian W. Gardner,et al.  Performance definition and standardization of electronic noses , 1996 .

[37]  Karol Nitsch,et al.  Characterisation of electrical parameters for multilayer SnO2 gas sensors , 2004 .

[38]  Koji Moriya,et al.  Indium oxide-based gas sensor for selective detection of CO , 1996 .

[39]  Thomas G. Dietterich.  Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[40]  K. Yoshikawa,et al.  Gas Sensing Based on a Nonlinear Response:  Discrimination between Hydrocarbons and Quantification of Individual Components in a Gas Mixture. , 1996, Analytical chemistry.