A calibrant-free drift compensation method for gas sensor arrays

Gas sensors lack repeatability over time. They are affected by drift, the result of changes at the sensor level and in the environment. A solution is to design software methods that compensate for the drift. Existing methods are often based on calibration samples acquired at the start of each new measurement session. However, finding a good reference compound is a difficult task and generating calibration samples is time-consuming. We propose a model-based correction method which does not require any calibration sample over time, operating ‘blindly’. In this study, we focus on the drift affecting electronic noses. To this end, we built a real data set acquired over 9 months in real-life conditions. By using the proposed method, we show that the drift is partly compensated, thus increasing the reliability of the electronic nose. Besides, we also show that the algorithm can easily adapt if the target compounds are not all sampled during every session. • We propose a drift correction method which does not require any calibration samples over time. • Our algorithm is based on a drift model and on the Expectation-Maximisation method to estimate model parameters. • We present a data set acquired over 9 months in real-life conditions using an opto-electronic nose. • Using the proposed algorithm, we can partly compensate for the drift, increasing the reliability of the optoelectronic nose.