Non-Local Patch Regression Algorithm-Enhanced Differential Photoacoustic Methodology for Highly Sensitive Trace Gas Detection
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 dramaticall...
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MDPI AG
2021-09-01
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| Series: | Chemosensors |
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| Online Access: | https://www.mdpi.com/2227-9040/9/9/268 |
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| author | Le Zhang Lixian Liu Huiting Huan Xukun Yin Xueshi Zhang Andreas Mandelis Xiaopeng Shao |
| author_facet | Le Zhang Lixian Liu Huiting Huan Xukun Yin Xueshi Zhang Andreas Mandelis Xiaopeng Shao |
| author_sort | Le Zhang |
| collection | DOAJ |
| description | 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 C<sub>2</sub>H<sub>2</sub> in the background of ambient air. A normalized noise equivalent absorption (NNEA) coefficient of 6.13 × 10<sup>−11</sup> cm<sup>−1</sup>·W·Hz<sup>−1/2</sup> 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 C<sub>2</sub>H<sub>2</sub>, H<sub>2</sub>O, and CO<sub>2</sub>. 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 C<sub>2</sub>H<sub>2</sub>, H<sub>2</sub>O, and CO<sub>2</sub> spectra were improved by a factor of 20. The NNEA coefficient reached a value of 7.02 × 10<sup>−11</sup> cm<sup>−1</sup>·W·Hz<sup>−1/2</sup> for C<sub>2</sub>H<sub>2</sub>. 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. |
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| last_indexed | 2024-03-10T07:48:41Z |
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| spelling | doaj.art-b4bc3eb6c357465594248e57591358dc2023-11-22T12:28:58ZengMDPI AGChemosensors2227-90402021-09-019926810.3390/chemosensors9090268Non-Local Patch Regression Algorithm-Enhanced Differential Photoacoustic Methodology for Highly Sensitive Trace Gas DetectionLe Zhang0Lixian Liu1Huiting Huan2Xukun Yin3Xueshi Zhang4Andreas Mandelis5Xiaopeng Shao6School of Physics and Optoelectronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Physics and Optoelectronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Physics and Optoelectronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Physics and Optoelectronic Engineering, Xidian University, Xi’an 710071, ChinaSchool of Physics and Optoelectronic Engineering, Xidian University, Xi’an 710071, ChinaCenter for Advanced Diffusion-Wave and Photoacoustic Technologies (CADIPT), Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, CanadaSchool of Physics and Optoelectronic Engineering, Xidian University, Xi’an 710071, ChinaA 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 C<sub>2</sub>H<sub>2</sub> in the background of ambient air. A normalized noise equivalent absorption (NNEA) coefficient of 6.13 × 10<sup>−11</sup> cm<sup>−1</sup>·W·Hz<sup>−1/2</sup> 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 C<sub>2</sub>H<sub>2</sub>, H<sub>2</sub>O, and CO<sub>2</sub>. 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 C<sub>2</sub>H<sub>2</sub>, H<sub>2</sub>O, and CO<sub>2</sub> spectra were improved by a factor of 20. The NNEA coefficient reached a value of 7.02 × 10<sup>−11</sup> cm<sup>−1</sup>·W·Hz<sup>−1/2</sup> for C<sub>2</sub>H<sub>2</sub>. 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.https://www.mdpi.com/2227-9040/9/9/268photoacoustic spectroscopygas sensorsmulti-componentnon-local denoising algorithm |
| spellingShingle | Le Zhang Lixian Liu Huiting Huan Xukun Yin Xueshi Zhang Andreas Mandelis Xiaopeng Shao Non-Local Patch Regression Algorithm-Enhanced Differential Photoacoustic Methodology for Highly Sensitive Trace Gas Detection Chemosensors photoacoustic spectroscopy gas sensors multi-component non-local denoising algorithm |
| title | Non-Local Patch Regression Algorithm-Enhanced Differential Photoacoustic Methodology for Highly Sensitive Trace Gas Detection |
| title_full | Non-Local Patch Regression Algorithm-Enhanced Differential Photoacoustic Methodology for Highly Sensitive Trace Gas Detection |
| title_fullStr | Non-Local Patch Regression Algorithm-Enhanced Differential Photoacoustic Methodology for Highly Sensitive Trace Gas Detection |
| title_full_unstemmed | Non-Local Patch Regression Algorithm-Enhanced Differential Photoacoustic Methodology for Highly Sensitive Trace Gas Detection |
| title_short | Non-Local Patch Regression Algorithm-Enhanced Differential Photoacoustic Methodology for Highly Sensitive Trace Gas Detection |
| title_sort | non local patch regression algorithm enhanced differential photoacoustic methodology for highly sensitive trace gas detection |
| topic | photoacoustic spectroscopy gas sensors multi-component non-local denoising algorithm |
| url | https://www.mdpi.com/2227-9040/9/9/268 |
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