Local Gaussian process regression with small sample data for temperature and humidity compensation of polyaniline-cerium dioxide NH3 sensor

Gas sensors have made great progress in gas sensing performances (such as response, sensitivity, detection limit and response speed), but they are generally affected by temperature and humidity. Here, we proposed a local Gaussian process regression with small samples for temperature and humidity compensation of gas sensors. Specifically, the above method is used to compensate the temperature and humidity influence of the polyaniline-cerium dioxide (PANI-CeO2) ammonia (NH3) sensor (10–50 °C, 20%−70% relative humidity (RH); It should be noted that the law of humidity influence is messy when the RH is greater than 70%.). The adaptive matching results show that the optimal number of K-neighbor points is 15, which greatly reduces the amount of computation. The temperature and humidity compensation results show that the predicted concentration achieved high accuracy (the mean absolute error is 0.19 ppm, and the mean relative error is 0.65%), and the absolute error showed a good normal distribution (N∼(0.00047, 0.3772)). This work provides an effective compensation strategy with small samples, high precision and low computational cost for the temperature and humidity influences of gas sensor.