TY - JOUR N2 - 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. L1 - http://www.czasopisma.pan.pl/Content/90351/PDF/Journal10178-Volume%20XXII%20Issue3_02paper.pdf L2 - http://www.czasopisma.pan.pl/Content/90351 PY - 2015 IS - No 3 EP - 350 DO - 10.1515/mms-2015-0039 KW - LS-SVM algorithm KW - resistance gas sensor KW - fluctuation enhanced sensing KW - gas detection A1 - Lentka, Ɓukasz A1 - Smulko, Janusz M. A1 - Ionescu, Radu A1 - Granqvist, Claes G. A1 - Kish, Laszlo B. PB - Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation VL - vol. 22 DA - 2015[2015.01.01 AD - 2015.12.31 AD] T1 - Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm SP - 341 UR - http://www.czasopisma.pan.pl/dlibra/publication/edition/90351 T2 - Metrology and Measurement Systems ER -