Finding the best calibration points for a gas sensor array with support vector regression

Electronic noses and gas alarm systems use chemical sensor arrays for the detection of gas mixtures. These sensing devices typically have a high degree of collinearity and nonlinear responses which makes their calibration difficult. Support vector regression was used to select a minimal number of calibration points for a dataset generated from laboratory measurements of a twelve element metal oxide sensor array exposed to ternary mixtures of CO, CH₄, and ethanol. The results indicate that the prediction accuracy of the model generated with kernel regression methods is better than that of partial least squares even when the number of calibration points is small.