Metrology and Measurement Systems · 2015 · 65 citations · 18 references
Natural SciencesMaterials EngineeringMaterials ScienceAnalytical ChemistrySensorsSpectroscopic AnalysisChemical Composition AnalysisChemical SensorsGas SensorsSensor MaterialsSensor-based AnalysisSupport Vector MachinesGas DetectionGas Detection TechnologyGas SensingGas Sensor Signal ProcessingNonlinear RegressionNonlinear Regression ModelsSensor CharacterizationUltraviolet Irradiation
Abstract This paper analyses the effectiveness of determining gas concentrations by using a prototype WO 3 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 WO 3 layer. Fluctuation-enhanced sensing allowed us to predict the concentration of both component gases.
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