Non-Local Patch Regression Algorithm-Enhanced Differential Photoacoustic Methodology for Highly Sensitive Trace Gas Detection

• Main Authors: Le Zhang, Lixian Liu, Huiting Huan, Xukun Yin, Xueshi Zhang, Andreas Mandelis, Xiaopeng Shao
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Chemosensors
• Subjects: photoacoustic spectroscopy; gas sensors; multi-component; non-local denoising algorithm
• Online Access: https://www.mdpi.com/2227-9040/9/9/268

A non-local patch regression (NLPR) denoising-enhanced differential broadband photoacoustic (PA) sensor was developed for the high-sensitive detection of multiple trace gases. Using the edge preservation index (EPI) and signal-to-noise ratio (SNR) as a dual-criterion, the fluctuation was dramatically suppressed while the spectral absorption peaks were maintained by the introduction of a NLPR algorithm. The feasibility of the broadband framework was verified by measuring the C₂H₂ in the background of ambient air. A normalized noise equivalent absorption (NNEA) coefficient of 6.13 × 10⁻¹¹ cm⁻¹·W·Hz^−1/2 was obtained with a 30-mW globar source and a SNR improvement factor of 23. Furthermore, the simultaneous multiple-trace-gas detection capability was determined by measuring C₂H₂, H₂O, and CO₂. Following the guidance of single-component processing, the NLPR processed results showed higher EPI and SNR compared to the spectra denoised by the wavelet method and the non-local means algorithm. The experimentally determined SNRs of the C₂H₂, H₂O, and CO₂ spectra were improved by a factor of 20. The NNEA coefficient reached a value of 7.02 × 10⁻¹¹ cm⁻¹·W·Hz^−1/2 for C₂H₂. The NLPR algorithm presented good performance in noise suppression and absorption peak fidelity, which offered a higher dynamic range and was demonstrated to be an effective approach for trace gas analysis.