Highly sensitive HF detection based on absorption enhanced light-induced thermoelastic spectroscopy with a quartz tuning fork of receive and shallow neural network fitting

Due to its advantages of non-contact measurement and high sensitivity, light-induced thermoelastic spectroscopy (LITES) is one of the most promising methods for corrosive gas detection. In this manuscript, a highly sensitive hydrogen fluoride (HF) sensor based on LITES technique is reported for the first time. With simple structure and strong robustness, a shallow neural network (SNN) fitting algorithm is introduced into the field of spectroscopy data processing to achieve denoising. This algorithm provides an end-to-end approach that takes in the raw input data without any pre-processing and extracts features automatically. A continuous wave (CW) distributed feedback diode (DFB) laser with an emission wavelength of 1.27 mu m was used as the excitation source. A Herriott multi-pass cell (MPC) with an optical length of 10.1 m was selected to enhance the laser absorption. A quartz tuning fork (QTF) with resonance frequency of 32,767.52 Hz was adopted as the thermoelastic detector. An Allan variance analysis was performed to demonstrate the system stability. When the integration time was 110 s, the minimum detection limit (MDL) was found to be 71 ppb. After the SNN fitting algorithm was used, the signal-to-noise ratio (SNR) of the HF-LITES sensor was improved by a factor of 2.0, which verified the effectiveness of this fitting algorithm for spectroscopy data processing.

Automated summary

A highly sensitive hydrogen fluoride (HF) sensor based on light-induced thermoelastic spectroscopy (LITES) technique is reported in this study. The sensor utilizes a simple structure and a robust shallow neural network (SNN) fitting algorithm for denoising the spectroscopy data. The system stability is demonstrated through Allan variance analysis and a low minimum detection limit (MDL) is achieved.