Rapid Classification of Steel by a Mobile Laser-Induced Breakdown Spectroscopy Based on Optical Fiber Delivering Laser Energy
LI Wen-xin1, CHEN Guang-hui1, 3, ZENG Qing-dong1, 2*, YUAN Meng-tian1, 3, HE Wu-guang1, JIANG Ze-fang1, LIU Yang1, NIE Chang-jiang1, YU Hua-qing1, GUO Lian-bo2
1. School of Physics and Electronic-Information Engineering, Hubei Engineering University, Xiaogan 432000, China
2. Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan 430074, China
3. Faculty of Physics & Electronic Sciences, Hubei University, Wuhan 430062, China
Abstract：In order to realize the industrial on-site rapid detection and identification for special steel, a mobile laser-induced breakdown spectroscopy prototype based on optical fiber delivering laser energy is adopted in this experiment to collect the spectral data of 14 special sheets of steel. The spectra of special steels were rapidly classified via dimensionality reduction in which pre-selected spectral lines were traversed, combined with a support vector machine (SVM).In the experiment, original spectral data, normalized spectral data and normalized spectral data after traversed were used as the input vectors of the SVM classification model, and the recognition accuracy of the model for special steels under different input vectors was compared. The results show that on the basis that more than 51 spectral lines were selected as input variables, the recognition accuracy of normalized spectral data as input variables for steels reaches 95.71%. It is significantly higher than 11.43%, whose accuracy was used raw spectral data as the input vector. Further, the MATLAB program was used to traverse the spectral line combination to choose the optimal input features. When 6 specific spectral lines were selected, the accuracy of special steels recognition reached 100%, and the modeling speed was also improved accordingly. It can be seen that when a large number of common feature data are pre-selected, automatic feature selection by machine has obvious advantages over the spectral line of manual selection. The SVM algorithm based on this dimension reduction method has a good industrial application prospect in LIBS rapid classification technology.
Key words：Laser induced Breakdown spectroscopy; SVM; Spectral line traversal combination; Dimension reduction; Classification of steel
李文鑫，陈光辉，曾庆栋，袁梦甜，何武光，江泽方，刘 洋，聂长江，余华清，郭连波. 光纤传能的移动式激光诱导击穿光谱钢铁快速检测与分类[J]. 光谱学与光谱分析, 2021, 41(08): 2638-2643.
LI Wen-xin, CHEN Guang-hui, ZENG Qing-dong, YUAN Meng-tian, HE Wu-guang, JIANG Ze-fang, LIU Yang, NIE Chang-jiang, YU Hua-qing, GUO Lian-bo. Rapid Classification of Steel by a Mobile Laser-Induced Breakdown Spectroscopy Based on Optical Fiber Delivering Laser Energy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2638-2643.
[1] Zeng Q D, Guo L B, Li X Y, et al. Journal of Analytical Atomic Spectrometry, 2016, 31(3): 767.
[2] Sturm V, Fleige, Rüdiger, De Kanter M, et al. Analytical Chemistry, 2014, 86(19): 9687.
[3] ZENG Qing-dong, ZHU Zhi-heng, DENG Fan, et al(曾庆栋, 朱志恒, 邓 凡, 等). Acta Photonica Sinica(光子学报), 2018, 47(8): 102.
[4] Distjerdi M V, Mousavi S J, Soltanolkotabi, et al. Iran J. Sci. Technol. Trans. Sci., 2018, 42: 959.
[5] Zeng Q D, Guo L B, Li X Y, et al. Journal of Analytical Atomic Spectrometry, 2015, 30(2): 403.
[6] Zeng Q D, Deng F, Zhu Z H, et al. Plasma Science & Technology, 2019, 21(3): 034006.
[7] Zou X H, Guo L B, Shen M, et al. Optics Express, 2014, 22(9): 10233.
[8] Guo L B, Zhang B Y, He X N, et al. Optics Express, 2012, 20(2): 1436.
[9] Hahn D W, Omenetto N. Applied Spectroscopy, 2012, 66(4): 347.
[10] Yang Y W, Hao X J, Zhang L L, et al. Sensors, 2020, 20: 1393.
[11] Kasem M A, Gonzalez J J, Russo R E, et al. Talanta, 2013, 108: 53.
[12] YANG You-sheng, ZHANG Yan, YANG You-liang, et al(杨友盛, 张 岩, 杨友良, 等). Laser & Optoelectronics Progress(激光与光电子学进展), 2015, 52(5): 209.
[13] GU Yan-hong, ZHAO Nan-jing, MA Ming-jun, et al(谷艳红, 赵南京, 马明俊, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(6): 1893.
[14] Cortes C, Vapnik V . Machine Learning, 1995, 20(3): 273.
[15] Zhang T, Wu S, Dong J, et al. Journal of Analytical Atomic Spectrometry, 2015, 30(2): 368.
