Machine Learning Based Wavelength Modulation Spectroscopy for Rapid Gas Sensing

As a non-intrusive, fast-response and highly sensitive and diagnostic tool, Wavelength Modulation Spectroscopy (WMS) has been extensively applied in accurate retrieval of gas properties, e.g. species concentration and temperature. Using the calibration-free WMS (CF-WMS) strategy, the first harmonic normalised second harmonic signal, e.g. 2f/1f of the modulated laser transmission is extracted, and then fitted to calculate the path-integrated absorbance. However, the fitting process mainly suffers from (a) noise in the fitting results introduced by the shift of the centre wavelength of the laser, and (b) a relatively high computational cost due to the least square optimisation. To improve the measurement precision and efficiency, this paper proposes a machine learning regression algorithm to calculate the gas properties. The proposed method employs artificial neural networks (ANN) to compute the path-integrated absorbance rapidly with a high signal-to-noise ratio, which was experimentally validated by calculating the absorption of water vapour at the wavelength of 1391.2 nm. In comparison with the traditional fitting method, the proposed machine learning based WMS is two times more noise-resistant with high capability to compute 100 sets of 2fl1f signals in approximately 0.4s, denoting its potential applicability in real-time and rapid trace gas sensing.