Smart Gas Sensing using Single MOS Gas Sensor with Adaptive Gradient Boosting

The ever-increasing demand of smart sensors for Internet of things applications, drove the change in outlook towards smart sensor system design. This paper focuses on smart sensing approach using single pristine tin-oxide (SnO
2
) sensing film based sensor. It is a novel attempt to not only identify but also quantify the bio-marker gases like ammonia (NH
3
), carbon monoxide (CO) and hydrogen sulphide (H
2
S) using single sensor with intelligent pattern recognition algorithms and its readout circuit. The presented work uses temperature modulated gas sensor response obtained at different concentrations for mentioned gases with in-house developed sensor for gas discrimination and quantification. This work uses wavelet features with Independent Component Analysis (ICA) based feature extraction technique and proposed Adaptive Gradient Boosting classifier for gas sensor response pattern recognition. The proposed approach provides gas detection accuracy 93.28%. Further, our approach uses maximum likelihood fit to quantify concentration of the predicted gas and reported the average MSE for all three gases is 0.11, which makes the proposed work smart to mitigate the cross-selectivity of MOS based smart sensor design in presence of drift and noise.