On-line drift compensation for continuous monitoring with arrays of cross-sensitive chemical sensors

Long-term application of chemical sensor arrays for continuous monitoring is challenging as a result of sensor drift. Drift correction often requires periodic recalibration, which may not be feasible for sensors deeply embedded and deployed for uninterrupted continuous monitoring. In this paper, we propose a multi-calibration ensemble approach to compensate for sensor drift in such applications. Our method uses past sensor measure-ments for which ground-truth is available, and treats them as pseudo-calibration samples. With these, it builds a regression model to predict the concentration of target analytes by combining (1) the current sensor mea-surements and (2) the history of prior pseudo-calibration samples. We evaluate the efficacy of the proposed model using three different regression techniques, partial least squares, extreme gradient boosting, and neural networks, and compare it against two baselines: regression models that do not use the pseudo-calibration samples, and a state-of-the-art drift-correction technique. We evaluated these models on an experimental data-set from a bioprocess control application, and characterize them as a function of cross-sensitivity in the sensor array and amount of drift through computer simulations. The proposed approach outperforms both baselines on the experimental dataset, and under all simulation conditions, achieving significantly lower normalized root mean square errors in the prediction of target variables. These results hold for the three regression models used, which indicates that the proposed approach is agnostic to the underlying regression model.

Automated summary

In this paper, a multi-calibration ensemble approach is proposed to compensate for sensor drift in long-term application of chemical sensor arrays. The method utilizes past sensor measurements and known ground-truth data to build a regression model for predicting the concentration of target analytes. Experimental and simulation results demonstrate the superiority of the proposed approach compared to existing methods under various conditions.