Multiparameter gas sensor based on UV-SFR combined with wavelet transform-neural network at ppb level

Abstract

Abnormal concentrations of nitric oxide (NO), ammonia (NH3), carbon disulfide (CS2) and sulphur dioxide (SO2) in exhaled breath are closely associated with diseases in specific organs of the body. Meanwhile, the above gases are also common pollutants in the atmosphere, and effective online detection of them plays a key role in disease prevention and environmental pollution prevention. This study reports a multiparameter gas sensor system based on the combination of ultraviolet segmented fitting reduction (UV-SFR) and wavelet transform-neural network (WT-NN) model for simultaneous online detection of the concentrations of NO, NH3, CS2 and SO2. Firstly, to avoid the occurrence of spectral baseline drift and absorption peak intensity reduction, a UV-SFR method is proposed for the discrete single peak type absorption spectra contained in the UV spectra. Our method can remove the slow change absorption induced by other factors, and ultimately obtain the differential absorption spectra of the gas mixtures. Then, the WT is used to denoise the differential absorption spectra of the gas mixtures. Next, for the presence of severe spectral line overlap in gas mixtures, the NN model is built based on the denoised spectral data. Finally, transfer learning is introduced into NN to improve the detection accuracy. The experimental results show that the sensor system can reliably achieve simultaneous online detection of NO, NH3, CS2, and SO2 at ppb level, and the average absolute errors of their measurements are 0.96 ppb, 1.27 ppb, 0.76 ppb and 0.97 ppb, with measurement accuracies of 3.5 %, 0.8 %, 0.5 % and 0.9 %, respectively.