Drift compensation of gas sensor array data by common principal component analysis

Abstract

A new drift compensation method based on common principal component analysis (CPCA) is proposed. The drift variance in data is found as the principal components computed by CPCA. This method finds components that are common for all gasses in feature space. The method is compared in classification task with respect to the other approaches published where the drift direction is estimated through a principal component analysis (PCA) of a reference gas. The proposed new method – employing no specific reference gas, but information from all gases – has shown the same performance as the traditional approach with the best-fitted reference gas. Results are shown with data lasting 7 months including three gases at different concentrations for an array of 17 polymeric sensors.