Elemental Determination in Stainless Steel via Laser-Induced Breakdown Spectroscopy and Back-Propagation Artificial Intelligence Network with Spectral Pre-Processing

• Published in: Chemosensors Vol. 10; no. 11; p. 472
• Main Authors: Ni, Yang, Fan, Bowen, Fang, Bin, Meng, Jiuling, Zhang, Yubo, Lü, Tao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Algorithms; Artificial intelligence; Back propagation; Back propagation networks; back-propagation artificial intelligence network; Calibration; Chromium; data pre-processing; Energy; Error reduction; Experiments; Interpolation; Laser induced breakdown spectroscopy; Lasers; Methods; Neural networks; Nickel; Plasma; Propagation; Quantitative analysis; Radiation, Background; Signal processing; Signal to noise ratio; Spectroscopy; Spectrum analysis; Stainless steel; Stainless steels; Titanium; Wavelet transforms; China
• ISSN: 2227-9040; 2227-9040
• DOI: 10.3390/chemosensors10110472

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.