| Autor: |
Yang Ni, Bowen Fan, Bin Fang, Jiuling Meng, Yubo Zhang, Tao Lü |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Chemosensors, Vol 10, Iss 11, p 472 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2227-9040 |
| DOI: |
10.3390/chemosensors10110472 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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