Drift Reduction For Metal-Oxide Sensor Arrays Using Canonical Correlation Regression And Partial Least Squares

Abstract. The transient response of metal-oxide sensors exposed to mild odours can be oftentimes highly correlated with the behaviour of the array during the preceding wash and reference cycles. Since wash/reference gases are virtually constant overtime, variations in their transient response can be used to estimate the amount of sensor drift present in each experiment. We perform canonical correlation analysis and partial least squares to find a subset of “latent variables” that summarize the linear dependencies between odour and wash/reference responses. Ordinary least squares regression is then used to subtract these “latent variables” from the odour response. Experimental results on an odour database of four cooking spices, collected on a 10-sensor array over a period of three months, show significant improvements in predictive accuracy.