Global calibration models for temperature-modulated metal oxide gas sensors: A strategy to reduce calibration costs

Abstract

Tolerances in the fabrication of metal oxide (MOX) gas sensors lead to inter-device variability in baseline and sensitivity, even for sensors of the same fabrication batch. This has traditionally forced the use of individual calibration models (ICMs) built specifically for each sensor unit, which requires an expensive and time-consuming calibration process and hinders sensor replacement. We propose Global calibration models (GCMs) built using the responses of multiple sensor units, and then applied to a new sensor unit that is not part of the calibration set. GCM have been already successfully applied to transfer calibration models between sensor arrays (electronic noses) for classification tasks. In this work, we investigate the use of such models for regression purposes in temperature-modulated sensors, aiming at the quantification of low concentrations of carbon monoxide (CO) in the presence of variable humidity levels (20–80% r.h. at 26 ± 1 °C). Using a laboratory dataset containing data from 6 replicas of the FIS SB-500–12 model, we evaluate the performance of global models built with data from 1 to 4 sensors when applied to unseen sensor units. Results show that the performance of global models improves with an increasing number of sensors in the calibration set, approaching the performance of individual calibration models (1.38 ± 0.15 ppm for GCM; 1.05 ± 0.24 ppm for ICM), and surpassing their performance only if few calibration conditions per sensor are available (2.09 ± 0.10 ppm for GCM;; 2.76 ± 0.22 ppm for ICM, if only 5 samples per sensor are used).