Interpretable Calibration Transfer and Drift Compensation for MOS Gas Sensors in Complex Gas Mixtures.

This study presents a novel approach for model-based calibration transfer and drift compensation for metal oxide semiconductor (MOS) gas sensors. The sensors are lab-calibrated and different calibration models for each of the eight volatiles contained in the calibration are trained to allow for an interpretable quantification of individual volatiles in complex mixtures. Calibration transfer and drift compensation are used to compensate for domain shifts that particularly affect the model accuracy. Here, several domain shifts are considered, e.g., sensor-to-sensor variation among different production batches (calibration transfer) or time-related changes in sensor response like poisoning and aging (drift compensation). Such domain shifts can lead to a substantial performance degradation and are critical for reliable field deployment. Since interpretable and robust machine learning algorithms based on feature extraction, feature selection, and regression (FESR) are not inherently capable of model-based calibration transfer and drift compensation, recalibration typically requires time-consuming and labor-intensive laboratory calibration procedures. To address this challenge, a novel approach represents the interpretable FESR machine learning models as a deep neural network (IDNNRep), enabling the application of transfer learning techniques from the field of deep neural networks (DNNs). This allows the reuse of knowledge gained in an initial calibration domain and facilitates model transfer using only a small amount of new calibration data, thereby reducing calibration effort and time. The proposed method is evaluated across multiple gases, including acetone and toluene, for four domain-shift scenarios and compared with FESR models retrained exclusively on data from the new domain and orthogonal signal correction (OSC). The results demonstrate that the proposed approach reduces the root mean square error (RMSE) compared to the initial model, achieving values of 18.0-28.0 ppb (normalized RMSE: 6.3-9.3%) for both gases with only 0.1 of the calibration data, resulting in a reduction of up to 93% compared to the initial calibration model and 89% compared to the OSC. Furthermore, due to the interpretable nature of the underlying FESR structure, the calibration transfer enables additional sensor- and gas-specific insights.