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Calibration of Microhotplate Conductometric Gas Sensors by Non-Linear Multivariate Regression Methods



B K. Dable, K S. Booksh, Richard E. Cavicchi, Stephen Semancik


This paper presents the first demonstration of quantitative multicomponent multivariate calibration of microhotplate conductometric sensors for binary and tertiary mixtures of light gases in air. Four element sensors of TiO2, SnO2 with a layer of gold, and 2 different grain clusters of SnO2 were used to differentiate among the analytes in the mixtures. We illustrate results from isothermal operation of these varied sensors as well as the value of high-information content operationin dynamic temperature programmed settings where the rate response change is dependent on the kinetic response of each sensing layer to the gas. The conductometric sensors are shown to have a marked non-linear profile with change in concentration. Several non-linear multivariate regression methods have been investigated to best calibrate the resulting signals from the mixtures of analyte gasses: locally weighted regression (LWR), alternating conditional expectation (ACE), and projection pursuit (PP). In the best scenario, these non-linear regression methods have predicted mixtures of methanol and hydrogen gas to within 10 ppm when calibrated within a concentration range of 0 - 150 ppm.
Sensors and Actuators B-Chemical
No. 3


array, conductance, electronic nose, gas microsensor, kinetics, microhotplate, nonlinear calibration, tin oxide


Dable, B. , Booksh, K. , Cavicchi, R. and Semancik, S. (2004), Calibration of Microhotplate Conductometric Gas Sensors by Non-Linear Multivariate Regression Methods, Sensors and Actuators B-Chemical (Accessed May 25, 2024)


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Created June 30, 2004, Updated October 12, 2021