Elemental Determination in Stainless Steel via Laser-Induced Breakdown Spectroscopy and Back-Propagation Artificial Intelligence Network with Spectral Pre-Processing
Minor elements significantly influence the properties of stainless steel. In this study, a laser-induced breakdown spectroscopy (LIBS) technique combined with a back-propagation artificial intelligence network (BP-ANN) was used to detect nickel (Ni), chromium (Cr), and titanium (Ti) in stainless ste...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Chemosensors |
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| Online Access: | https://www.mdpi.com/2227-9040/10/11/472 |
| _version_ | 1797465643439620096 |
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| author | Yang Ni Bowen Fan Bin Fang Jiuling Meng Yubo Zhang Tao Lü |
| author_facet | Yang Ni Bowen Fan Bin Fang Jiuling Meng Yubo Zhang Tao Lü |
| author_sort | Yang Ni |
| collection | DOAJ |
| description | Minor elements significantly influence the properties of stainless steel. In this study, a laser-induced breakdown spectroscopy (LIBS) technique combined with a back-propagation artificial intelligence network (BP-ANN) was used to detect nickel (Ni), chromium (Cr), and titanium (Ti) in stainless steel. For data pre-processing, cubic spline interpolation and wavelet threshold transform algorithms were used to perform baseline removal and denoising. The results show that this set of pre-processing methods can effectively improve the signal-to-noise ratio, remove the baseline of spectral baseline, reduce the average relative error, and reduce relative standard deviation of BP-ANN predictions. It indicates that BP-ANN combined with pre-processing methods has promising applications for the determination of Ni, Cr, and Ti in stainless steel with LIBS and improves prediction accuracy and stability. |
| first_indexed | 2024-03-09T18:24:27Z |
| format | Article |
| id | doaj.art-68aca36567144a1fadad9b164529fe8d |
| institution | Directory Open Access Journal |
| issn | 2227-9040 |
| language | English |
| last_indexed | 2024-03-09T18:24:27Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Chemosensors |
| spelling | doaj.art-68aca36567144a1fadad9b164529fe8d2023-11-24T07:59:44ZengMDPI AGChemosensors2227-90402022-11-01101147210.3390/chemosensors10110472Elemental Determination in Stainless Steel via Laser-Induced Breakdown Spectroscopy and Back-Propagation Artificial Intelligence Network with Spectral Pre-ProcessingYang Ni0Bowen Fan1Bin Fang2Jiuling Meng3Yubo Zhang4Tao Lü5School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, ChinaSchool of Mathematics and Physics, China University of Geosciences, Wuhan 430074, ChinaSchool of Mathematics and Physics, China University of Geosciences, Wuhan 430074, ChinaCollege of Physics and Electronic Information Engineering, Hubei Engineering University, Xiaogan 432000, ChinaSchool of Automation, China University of Geosciences, Wuhan 430074, ChinaSchool of Automation, China University of Geosciences, Wuhan 430074, ChinaMinor elements significantly influence the properties of stainless steel. In this study, a laser-induced breakdown spectroscopy (LIBS) technique combined with a back-propagation artificial intelligence network (BP-ANN) was used to detect nickel (Ni), chromium (Cr), and titanium (Ti) in stainless steel. For data pre-processing, cubic spline interpolation and wavelet threshold transform algorithms were used to perform baseline removal and denoising. The results show that this set of pre-processing methods can effectively improve the signal-to-noise ratio, remove the baseline of spectral baseline, reduce the average relative error, and reduce relative standard deviation of BP-ANN predictions. It indicates that BP-ANN combined with pre-processing methods has promising applications for the determination of Ni, Cr, and Ti in stainless steel with LIBS and improves prediction accuracy and stability.https://www.mdpi.com/2227-9040/10/11/472laser-induced breakdown spectroscopyback-propagation artificial intelligence networkstainless steeldata pre-processing |
| spellingShingle | Yang Ni Bowen Fan Bin Fang Jiuling Meng Yubo Zhang Tao Lü Elemental Determination in Stainless Steel via Laser-Induced Breakdown Spectroscopy and Back-Propagation Artificial Intelligence Network with Spectral Pre-Processing Chemosensors laser-induced breakdown spectroscopy back-propagation artificial intelligence network stainless steel data pre-processing |
| title | Elemental Determination in Stainless Steel via Laser-Induced Breakdown Spectroscopy and Back-Propagation Artificial Intelligence Network with Spectral Pre-Processing |
| title_full | Elemental Determination in Stainless Steel via Laser-Induced Breakdown Spectroscopy and Back-Propagation Artificial Intelligence Network with Spectral Pre-Processing |
| title_fullStr | Elemental Determination in Stainless Steel via Laser-Induced Breakdown Spectroscopy and Back-Propagation Artificial Intelligence Network with Spectral Pre-Processing |
| title_full_unstemmed | Elemental Determination in Stainless Steel via Laser-Induced Breakdown Spectroscopy and Back-Propagation Artificial Intelligence Network with Spectral Pre-Processing |
| title_short | Elemental Determination in Stainless Steel via Laser-Induced Breakdown Spectroscopy and Back-Propagation Artificial Intelligence Network with Spectral Pre-Processing |
| title_sort | elemental determination in stainless steel via laser induced breakdown spectroscopy and back propagation artificial intelligence network with spectral pre processing |
| topic | laser-induced breakdown spectroscopy back-propagation artificial intelligence network stainless steel data pre-processing |
| url | https://www.mdpi.com/2227-9040/10/11/472 |
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