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Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper analyses the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition of a gas mixture by using only a single sensor. In the present study, we apply Least-Squares Support-Vector-Machine-based (LS-SVM-based) nonlinear regression to determine the gas concentration of each constituent in a mixture. We confirmed that the accuracy of the estimated gas concentration could be significantly improved by applying temperature change and ultraviolet irradiation of the WO3 layer. Fluctuation-enhanced sensing allowed us to predict the concentration of both component gases.
Rocznik
Strony
341--350
Opis fizyczny
Bibliogr. 26 poz., rys., tab., wykr., wzory
Twórcy
autor
  • Gdańsk University of Technology, Faculty of Electronics, Telecommunications and Informatics, G. Narutowicza 11/12, 80-233 Gdańsk, Poland
autor
  • Gdańsk University of Technology, Faculty of Electronics, Telecommunications and Informatics, G. Narutowicza 11/12, 80-233 Gdańsk, Poland
autor
  • Rovira i Virgili University, ETSE-DEEEA, Department of Electronics, Carrer de l`Escorxador, 43003 Tarragona, Spain
  • Uppsala University, Department of Engineering Sciences, P.O. Box 534, SE-75121 Uppsala, Sweden
autor
  • Texas A&M University, Department of Electrical and Computer Engineering, College Station, TX 77843-3128, USA
Bibliografia
  • [1] Zakrzewska, K. (2001). Mixed oxides as gas sensors. Thin Solid Films, 391, 229-238.
  • [2] Osowski, S., Siwek, K., Grzywacz, T., Brudzewski, K. (2014). Differential electronic nose in on-line dynamic measurements. Metrol. Meas. Syst., 21(4), 649-662.
  • [3] Kish, L.B., Vajtai, R., Granqvist, C.G. (2000). Extracting information from noise spectra of chemical sensors: single sensor electronic noses and tongues. Sensors and Actuators B: Chemical, 71(1), 55-59.
  • [4] Kotarski, M., Smulko, J. (2009). Noise measurement set-ups for fluctuations-enhanced gas sensing. Metrol. Meas. Syst., 16(3), 457-464.
  • [5] Korotcenkov, G., Cho, B.K. (2013). Engineering approaches for the improvement of conductometric gas sensor parameters. Part 1. Improvement of sensor sensitivity and selectivity (short survey). Sensors and Actuators B: Chemical, 188, 709-728.
  • [6] Kalinowski, P., Woźniak, Ł., Strzelczyk, A., Jasinski, P., Jasinski, G. (2013). Efficiency of linear and nonlinear classifiers for gas identification from electrocatalytic gas sensor. Metrol. Meas. Syst., 20(3), 501-512.
  • [7] Trawka, M., Smulko, J., Hasse, L., Ionescu, R., Annanouch, F.E., Llobet, E., Granqvist, C.G., Kish, L.B. (2014). Fluctuation enhanced gas sensing using UV irradiated Au-nanoparticle-decorated WO3-nanowire films. Proc. of the 8th International Conference on Sensing Technology, Sept. 2-4, 2014, Liverpool, UK, 282-286.
  • [8] Barman, I., Dingari, N.C., Singh, G.P., Soares, J.S., Dasari, R.R., Smulko, J.M. (2012). Investigation of noise-induced instabilities in quantitative biological spectroscopy and its implications for noninvasive glucose monitoring. Analytical Chemistry, 84(19), 8149-8156.
  • [9] Ayhan, B., Kwan, C., Zhou, J., Kish, L.B., Benkstein, K.D., Rogers, P.H., Semancik, S. (2013). Fluctuation enhanced sensing (FES) with a nanostructured, semiconducting metal oxide film for gas detection and classification. Sensors and Actuators B: Chemical, 188, 651-660.
  • [10] Vapnik, V. (2000). The nature of statistical learning theory. New York: Springer Science & Business Media.
  • [11] Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J., Suykens, J.A.K., Van Gestel, T. (2002). Least squares support vector machines, 4, Singapore, World Scientific.
  • [12] Pardo, M., Sberveglieri, G. (2005). Classification of electronic nose data with support vector machines. Sensors and Actuators B: Chemical, 107(2), 730-737.
  • [13] Kwiatkowski, A., Czerwicka, M., Smulko, J., Stepnowski, P. (2014). Detection of denatonium benzoate (Bitrex) remnants in noncommercial alcoholic beverages by raman spectroscopy. Journal of Forensic Sciences, 59(5), 1358-1363.
  • [14] Adankon, M.M., Cheriet, M. (2009). Model selection for the LS-SVM. Application to handwriting recognition.Pattern Recognition, 42(12), 3264-3270.
  • [15] Pelckmans, K., Suykens, J.A., Van Gestel, T., De Brabanter, J., Lukas, L., Hamers, B., De Moor, B. (2002). LS-SVMlab: a matlab/c toolbox for least squares support vector machines.
  • [16] Chauchard, F., Cogdill, R., Roussel, S., Roger, J. M., Bellon-Maurel, V. (2004). Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes. Chemometrics and Intelligent Laboratory Systems, 71(2), 141-150.
  • [17] Kaur, R., Kumar, R., Gulati, A., Ghanshyam, C., Kapur, P., Bhondekar, A.P. (2012). Enhancing electronic nose performance: A novel feature selection approach using dynamic social impact theory and moving window time slicing for classification of Kangra orthodox black tea (Camellia Sinensis (L.) O. Kuntze). Sensors and Actuators B: Chemical, 166, 309-319.
  • [18] Kumar, R., Bhondekar, A.P., Kaur, R., Vig, S., Sharma, A., Kapur, P. (2012). A simple electronic tongue.Sensors and Actuators B: Chemical, 171, 1046-1053.
  • [19] Kuhn, H.W.; Tucker, A.W. (1951). Nonlinear programming. Proc. of the 2nd Berkeley Symposium. Berkeley, University of California Press, 1951, 481-492.
  • [20] Hazewinkel, M. (2001). Mercer theorem. Encyclopedia of Mathematics. Springer.
  • [21] Jolliffe, I. (2002). Principal component analysis. London: John Wiley & Sons, Ltd.
  • [22] Nguyen, M.H., De la Torre, F. (2010). Optimal feature selection for support vector machines. Pattern Recognition, 43(3), 584-591.
  • [23] Aydin, I., Karakose, M., Akin, E. (2011). A multi-objective artificial immune algorithm for parameter optimization in support vector machine. Applied Soft Computing, 11(1), 120-129.
  • [24] Mroczka, J. (2013). The cognitive process in metrology. Measurement, 46(8), 2896-2907.
  • [25] Kwiatkowski, A., Gnyba, M., Smulko, J., Wierzba, P. (2010). Algorithms of chemicals detection using raman spectra. Metrol. Meas. Syst., 17(4), 549-559.
  • [26] Granqvist, C.G., Green, S., Jonson, E.K., Marsal, R., Niklasson, G.A., Roos, A., Kish, L.B. (2008). Electrochromic foil-based devices: Optical transmittance and modulation range, effect of ultraviolet irradiation, and quality assessment by 1/f current noise. Thin Solid Films, 516(17), 5921-5926.
Uwagi
EN
We would like to thank Maciej Trawka, PhD student, for providing detailed noise records. This work was supported by the National Science Center, Poland, Grant Agreement DEC-2012/06/M/ST7/00444 “Detection of gases by means of nano-technological resistance sensors”. C. G. Granqvist was supported from the European Research Council under the European Community’s Seventh Framework Program (FP7/2007–2013)/ERC Grant Agreement No. 267234 “GRINDOOR”. R. Ionescu acknowledges support from the ‘Ramón y Cajal’ fellowship awarded by the Spanish Ministry for Science and Competiveness (MINECO).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-94e77571-9697-4c89-8d28-faeb2bc61f54
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