|
|
|
|
|
|
| Different Feature Selection Methods Combined With Laser-Induced Breakdown Spectroscopy Were Used to Quantify the Contents of Nickel, Titanium and Chromium in Stainless Steel |
| WU Zhuo1, 2, SU Xiao-hui3, FAN Bo-wen4, ZHU Hui-hui1, 2, ZHANG Yu-bo1, 2, FANG Bin3, WANG Yi-fan1, 2, LÜ Tao1, 2* |
1. School of Automation, China University of Geosciences, Wuhan 430074, China
2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
3. School of Future Technology, China University of Geosciences (Wuhan), Wuhan 430074, China
4. School of Mathematics and Physics, China University of Geosciences (Wuhan), Wuhan 430074, China
|
|
|
|
|
Abstract Laser-induced breakdown spectroscopy (LIBS) technology, as an effective tool for material composition analysis, has broad application value. However, due to the poor repeatability of LIBS and the influence of matrix and self-absorption effects, spectral data contains a large number of redundant features that are useless for quantitative analysis. To overcome the difficulty in improving prediction accuracy when using raw full spectrum data as model input, two feature engineering techniques (minimum absolute shrinkage and selection operator regression LASSO and sequential backward selection SBS) were combined with machine learning to achieve a quantitative analysis of nickel (Ni), titanium (Ti), and chromium (Cr) in stainless steel samples. This study used seven stainless steel samples with different element contents purchased from Steel Research Nanogram Testing Technology Co., Ltd. as the research objects. Seventy LIBS spectra were obtained, and four different data preprocessing methods were compared, including Maximum Minimum Normalization (MMN), Standard Normal Variation (SNV), Savitzky Golay Smooth Filtering (SG), and Internal Standard Method (IS). The preprocessing results were detected using Root Mean Square Error (RSME). Finally, Savitzky Golay smoothing filtering was chosen for spectral preprocessing. Effective variables were independently selected for different quantization elements when selecting features using LASSO and SBS algorithms. Then, three different feature combinations, namely full spectrum, LASSO selection feature, and SBS selection feature were used as inputs to the model. To verify the effectiveness of the feature selection method, partial least squares (PLS) Compare two different machine learning models using a Support Vector Machine (SVM). Evaluate the performance of different models using Average Relative Error (ARE) and Relative Standard Deviation (RSD). The results showed that the model inputs selected by the two feature selection methods showed better prediction accuracy and stability compared to full-spectrum inputs in different machine learning models. Among them, the LASSO-PLS model achieved the best prediction accuracy in the quantitative analysis of Ni, Ti, and Cr elements, with ARE of 3.50%, 2.66%, and 0.93%, and RSD of 4.55%, 5.23%, and 2.04%, respectively. Therefore, the LIBS combined with LASSO and SBS algorithms proposed in this article can accurately and stably quantify the Ni, Ti, and Cr elements in stainless steel, providing a reference for further exploring the application of LIBS combined with machine learning in stainless steel element quantification analysis scenarios.
|
|
Received: 2023-11-11
Accepted: 2024-03-15
|
|
|
|
Corresponding Authors:
LÜ Tao
E-mail: lvtaohn@126.com
|
|
[1] Costa V C, Augusto A S, Castro J P, et al. Quimica Nova, 2019, 42(5): 527.
[2] Chahrour O, Malone J. Protein and Peptide Letters, 2017, 24(3): 253.
[3] Talari A C S, Movasaghi Z, Rehman S, et al. Applied Spectroscopy Reviews, 2015, 50(1): 46.
[4] Porep J U, Kammerer D R, Carle R. Trends in Food Science & Technology, 2015, 46(2): 211.
[5] Li F, Ge L Q, Tang Z Y, et al. Applied Spectroscopy Reviews, 2020, 55(4): 263.
[6] Terán E J, Montes M L, Rodríguez C, et al. Microchemical Journal, 2019, 144: 159.
[7] Markiewicz-Keszycka M, Cama-Moncunill X, Casado-Gavalda M P, et al. Trends in Food Science & Technology, 2017, 65: 80.
[8] Agresti J, Indelicato C, Perotti M, et al. Molecules, 2022, 27(6): 1813.
[9] Millar S, Kruschwitz S, Wilsch G. Cement and Concrete Research, 2019, 117: 16.
[10] SUN Lan-xiang, WANG Wei, ZHANG Peng, et al(孙兰香, 汪 为, 张 鹏, 等). Metallurgical Analysis(冶金分析), 2021, 41(12): 58.
[11] Zhang Q H, Liu Y Z. Atomic Spectroscopy, 2022, 43(2): 174.
[12] LIU Yu-duo, SHEN Wei-hua, ZHU Zhi-qing, et al(刘雨朵, 沈卫华, 朱志庆, 等). Corrosion and Protection(腐蚀与防护), 2022, 43(12): 1.
[13] XU Xiu-qing, WANG Wei, CHEN Zhi-teng, et al(徐秀清, 王 玮, 陈之腾, 等). Materials Reports(材料导报), 2022, 36(14): 21030183.
[14] LI Long-bo, LI Zheng-xian, LIU Lin-tao, et al(李龙博, 李争显, 刘林涛, 等). Rare Metal Materials and Engineering(稀有金属材料与工程), 2021, 50(5): 1743.
[15] Mamonova A A, Baglyuk G A, Kurovskii V Y. Physics of Metals and Metallography, 2015, 116(6): 562.
[16] Barua A, Ahmed M U, Begum S. IEEE Access, 2023, 11: 14804.
[17] ZHAO Wen-ya, MIN Hong, LIU Shu, et al(赵文雅, 闵 红, 刘 曙, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(7): 1998.
[18] Zhang D X, Zhang H, Zhao Y, et al. Applied Spectroscopy Reviews, 2022, 57(2): 89.
[19] Brunnbauer L, Gajarska Z, Lohninger H, et al. TrAC-Trends in Analytical Chemistry, 2023, 159: 116859.
[20] ZHANG Ran-ran, YING Lu-na, ZHOU Wei-dong(张冉冉, 应璐娜, 周卫东). Chinese Journal of Quantum Electronics(量子电子学报), 2023, 40(3): 376.
[21] Zhang T L, Yan C H, Qi J, et al. Journal of Analytical Atomic Spectrometry, 2017, 32(10): 1960.
[22] Wei P F, Lu Z Z, Song J W. Reliability Engineering & System Safety, 2015, 142: 399.
[23] DING Yu, YANG Lin-yu, CHEN Jing, et al(丁 宇, 杨淋玉, 陈 靖, 等). Laser & Optoelectronics Progress(激光与光电子学进展), 2022, 59(13): 1314006.
[24] Tavares T R, Mouazen A M, Nunes L C, et al. Soil & Tillage Research, 2022, 216: 105250.
[25] Li M G, Ruan F Q, Li R R, et al. Microchemical Journal, 2022, 182: 107928.
[26] Luarte D, Myakalwar A K, Velásquez M, et al. Analytical Methods, 2021, 13(9): 1181.
[27] Zhao Y J, Huo X M. Wiley Interdisciplinary Reviews-Computational Statistics, 2023, 15(4). https://doi.org/10.1002/wics.1602.
[28] Tognoni E, Cristoforetti G. Optics and Laser Technology, 2016, 79: 164.
[29] Hahn D W, Omenetto N. Applied Spectroscopy, 2012, 66(4): 347.
[30] Guezenoc J, Gallet-Budynek A, Bousquet B. Spectrochimica Acta Part B: Atomic Spectroscopy, 2019, 160: 105688.
[31] Syvilay D, Wilkie-Chancellier N, Trichereau B, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2015, 114: 38.
[32] Yoon S, Choi J, Moon S J, et al. Applied Sciences-Basel, 2021, 11(15)：7154.
[33] Andries E. Journal of Chemometrics, 2013, 27(3-4): 50.
[34] Wong T T. Pattern Recognition, 2015, 48(9): 2839.
|
| [1] |
SHAO Yan1, 2, 3, LÜ Jin-guang1, 2*, LIN Jing-jun4, ZHENG Kai-feng1, 2, ZHAO Bai-xuan1, 2, ZHAO Ying-ze1, 2, CHEN Yu-peng1, 2, QIN Yu-xin1, 2, WANG Wei-biao1, 2, LIN Xiao-mei5, LIANG Jing-qiu1, 2*. Determination of Metal Elements in Lubricant Oil by Beeswax Sample Preparation Assisted Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(12): 3321-3326. |
| [2] |
HAN Mei1, 2, 3, JIA Yun-hai1*, DAI Lian-shuang4, HU Jing-yu1, ZHAO Lei1, ZHANG Xi5*, WEI Chen2, 3, WANG Hai-zhou1. In Situ Quantitative Analysis of Elements in X80 Pipeline Steel Welds[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(12): 3406-3413. |
| [3] |
WANG Sa1, 2, QU Liang1, 2*, ZHANG Li-fu3, GAO Yu3, LI Guang-hua1, 2, CHANG Jing-jing1, 2. Research on the Inverse Model of Paper Viscosity Based on Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(12): 3524-3533. |
| [4] |
TIE Wei-bo1, WANG Qi1*, GAN Xiu-shi2, WANG Xu2, HUANG Jun-chen1, YANG Song-tao1, ZHANG Song1. FTIR Quantitative Analysis of Evolution and Interaction of Plastic Layer in Coking Process[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(12): 3553-3559. |
| [5] |
YU Xin-ran1, 3, ZHAO Peng2, HUAN Ke-wei2, LI Ye2, JIANG Zhi-xia1, 3, ZHOU Lin-hua1, 3*. Research on Intelligent Algorithm of Near-Infrared Spectroscopy
Non-Invasive Detection Based on GA-SVR Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(11): 3020-3028. |
| [6] |
WANG Xiao-yan1, 2, JIANG Zhe-zhen1, JI Ren-dong1, 2*, BIAN Hai-yi1, 2, HE Ying1, CHEN Xu1, XU Chun-xiang3. Detection and Analysis of Mixed Organic Pesticides Based on
Three-Dimensional Fluorescence Spectroscopy and PARAFAC[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(11): 3082-3089. |
| [7] |
YAN Hong-yu1, ZHAO Yu2, CHEN Yuan-yuan2*, LIU Hao1, WANG Zhi-bin1. Explosive Residue Identification Study by Remote LIBS Combined With GA-arPLS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(11): 3199-3205. |
| [8] |
QU Dong-ming, ZHANG Zi-yi, LIANG Jun-xuan, LIAO Hai-wen, YANG Guang*. Classification of Copper Alloys Based on Microjoule High Repetition
Laser-Induced Breakdown Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(11): 3222-3227. |
| [9] |
WANG Xue1, 2, 4, WANG Zi-wen1, ZHANG Guang-yue1, MA Tie-min1, CHEN Zheng-guang1, YI Shu-juan3, 4, WANG Chang-yuan2. A Universal Model for Quantitative Analysis of Near-Infrared
Spectroscopy Based on Transfer Component Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(11): 3213-3221. |
| [10] |
ZHANG Jia-wei1, WU Dong-sheng1, ZHOU Yang2, LI Yang2, 3, 4, SUN Lan-xiang2, 3, 4*. Study on the Influence of Argon Environment on the Determination of C, P and S Elements in Steel by Laser-Induced Breakdown Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(10): 2834-2839. |
| [11] |
GUAN Cong-rong, LIANG Shuai, CHEN Ji-wen*, WANG Zhan-kuo. Study on LIBS Detection Method of Heavy Metal Split Type in Soil Based on Cluster Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2506-2513. |
| [12] |
HUANG Xiao-hong1, 2, LIU Xiao-chen1, 2, LIU Yan-li3*, SONG Chao1, 2, SUN Yong-chang1, 2, ZHANG Qing-jun4. Element Detection in Scrap Steel Using Portable LIBS and Sparrow Search Algorithm-Kernel Extreme Learning Machine (SSA-KELM)[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2412-2419. |
| [13] |
PENG Jiao-yu1, 2, YANG Ke-li1, 2, DONG Ya-ping1, 2, FENG Hai-tao1, 2, ZHANG Bo1, 3, LI Wu1, 3. Research on the Chemical Species of Borates in Salt Lake Brine and Its Quantitative Analysis by Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2514-2522. |
| [14] |
YANG Wen-feng, ZHENG Xin, LIN De-hui, QIAN Zi-ran, LI Shao-long, ZUO Du-quan, LI Guo, WANG Di-sheng. Design and Application Research of LIBS Monitoring Platform Based on High-Frequency Laser Paint Removal[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(09): 2600-2606. |
| [15] |
YANG Wen-feng, LI Guo, LIN De-hui, QIAN Zi-ran, LI Shao-long, ZUO Du-quan, ZHENG Xin, WANG Di-sheng. Research on Online Monitoring Criteria of Aircraft Skin Laser Paint
Removal Based on LIBS Data Flow Disk[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2340-2348. |
|
|
|
|
