We report a deep learning-assisted quartz-enhanced photoacoustic spectroscopy (QEPAS) sensor for trace water vapor detection in air. A 1392 nm butterfly-packaged DFB laser is wavelength-modulated at f0/2, and the QEPAS signal is retrieved by second-harmonic (2f) lock-in demodulation using a commercial quartz tuning fork gas cell. After optimizing the modulation depth to 400 mV, a 1D U-Net denoising network trained with pseudo-clean supervision is applied to the measured 2f traces, yielding an SNR improvement of 2.05× (3.11 dB). Allan deviation analysis indicates a minimum detection limit (MDL) of ~2.21 ppm at an optimum averaging time of ~619 s, corresponding to an ~2.1× improvement compared with the raw output. These results demonstrate that neural-network-based post-processing can improve QEPAS water vapor sensing performance without modifying the optical hardware.
Xiao et al. (Mon,) studied this question.