Transfer Learning Between Two Different Datasets of MOS Gas Sensors

Metal oxide semiconductor gas sensors are used in several applications, such as indoor air quality assessment, breath analysis, and industrial emission monitoring. In order to reach the full potential regarding the sensitivity and selectivity of those sensors for any application, temperature cycled operation, as well as careful calibration in the form of building a data-driven model is necessary. However, the calibration can be costly in terms of time and money as every sensor needs individual calibration. This work demonstrates that deep-learning based calibration transfer can be utilised to reuse the calibration model across different environmental scenarios for indoor air quality monitoring. Specifically, a sensor can be calibrated for new background conditions within six hours with the model achieving almost the same performance (RMSE: 16.4 ppb) as calibrated for 80 hours (RMSE: 14.8 ppb). This shows that it is possible to transfer a calibration between datasets with different gases and concentrations. The great reduction of the calibration time by 90 % or more would also allow in field calibration using reference analytical methods.