|
|
|
|
|
|
| Improvement of Convex Optimization Baseline Correction in Laser-Induced Breakdown Spectral Quantitative Analysis |
| KE Ke1, 2, Lü Yong1, 2, YI Can-can1, 2, 3* |
1. The State Key Laboratory of Refractories and Metallurgy, Wuhan University of Science and Technology, Wuhan 430081, China
2. School of Mechanical Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
3. State Key Laboratory of Refractory Materials and Metallurgy, Wuhan University of Science and Technology, Wuhan 430081, China |
|
|
|
|
Abstract In the laser-induced breakdown spectroscopy (LIBS) quantitative analysis technique, the baseline is an important part of the LIBS signal. Due to the fluctuation of laser energy and inhomogeneity of sample surfaces, the phenomenon of plasma emission line drift is obvious. Thus, baseline correction is an essential part during the spectrum data preprocessing. However, the existing algorithms do not have adaptability, thus there usually needs to set some key parameters, such as the suitable fitting function and order. In this paper, baseline correction and spectral signal denoising method are proposed on the basis of the data characteristics, such as the independence and sparsity of the spectral peak as well as the low-pass signal feature of baseline, which forme non-parametric baseline correction model under convex optimization framework. Meanwhile，efficient iterative algorithm is used to ensure the convergence of the results. In this paper, the spectral signal of 23 high alloy steel samples was firstly calibrated, and then the Cr element in the samples of alloy steel was analyzed. Subsequently, 11 analytical lines were selected for quantitative analysis. To verify the effectiveness of the proposed method, we utilized partial least squares (PLS) and support vector machine (SVM) quantitative model for training and prediction respectively. Compared with the traditional methods, the proposed method has a better performance in improving the quantitative analysis accuracy.
|
|
Received: 2017-07-12
Accepted: 2017-12-19
|
|
|
|
Corresponding Authors:
YI Can-can
E-mail: meyicancan@wust.edu.cn
|
|
[1] YANG Chun, JIA Yun-hai, CHEN Ji-wen, et al(杨 春，贾云海，陈吉文，等). Chinese Journal of Analytical Chemistry (分析化学), 2014(11).
[2] CHEN Xing-long, DONG Feng-zhong, TAO Guo-qiang, et al(陈兴龙，董凤忠，陶国强，等). Chinese Journal of Lasers(中国激光), 2013(12).
[3] Sturm V, Fleige R, de Kanter M, et al. Analytical Chemistry, 2014, 86(19): 9687.
[4] Feng J, Wang Z, Li L, et al. Applied Spectroscopy, 2013, 67(3): 291.
[5] HU Yang, LI Zi-han, Lü Tao(胡 杨, 李子涵, 吕 涛). Laser & Optoelectronics Progress(激光与光电子学进展), 2017, (5): 1.
[6] Yi C, Lv Y, Xiao H, et al. Journal of Analytical Atomic Spectrometry, 2017.
[7] Liang L, Zhang T, Wang K, et al. Applied Optics, 2014, 53(4): 544.
[8] Shi F, Ross P N, Zhao H, et al. Journal of the American Chemical Society, 2015, 137(9): 3181.
[9] LIU Li，XIAO Ping-ping(刘 莉，肖平平). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(2): 545.
[10] Myakalwar A K, Dingari N C, Dasari R R, et al. PloS One, 2014, 9(8): e103546.
[11] Sun L, Yu H. Spectrochimica Acta Part B: Atomic Spectroscopy, 2009, 64(3): 278.
[12] Yi C, Lv Y, Dang Z, et al. Applied Sciences, 2016, 6(12): 403.
[13] Yi C, Lv Y, Dang Z, et al. Measurement, 2017, 103: 321.
[14] Ning X, Selesnick I W, Duval L. Chemometrics and Intelligent Laboratory Systems, 2014, 139: 156.
[15] Yang J, Yi C, Xu J, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2015, 107: 45. |
| [1] |
LIU Jia1, 2, GUO Fei-fei2, YU Lei2, CUI Fei-peng2, ZHAO Ying2, HAN Bing2, SHEN Xue-jing1, 2, WANG Hai-zhou1, 2*. Quantitative Characterization of Components in Neodymium Iron Boron Permanent Magnets by Laser Induced Breakdown Spectroscopy （LIBS）[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 141-147. |
| [2] |
LIU Hao-dong1, 2, JIANG Xi-quan1, 2, NIU Hao1, 2, LIU Yu-bo1, LI Hui2, LIU Yuan2, Wei Zhang2, LI Lu-yan1, CHEN Ting1，ZHAO Yan-jie1*，NI Jia-sheng2*. Quantitative Analysis of Ethanol Based on Laser Raman Spectroscopy Normalization Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3820-3825. |
| [3] |
YANG Wen-feng1, LIN De-hui1, CAO Yu2, QIAN Zi-ran1, LI Shao-long1, ZHU De-hua2, LI Guo1, ZHANG Sai1. Study on LIBS Online Monitoring of Aircraft Skin Laser Layered Paint Removal Based on PCA-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3891-3898. |
| [4] |
LIN Hong-jian1, ZHAI Juan1*, LAI Wan-chang1, ZENG Chen-hao1, 2, ZHAO Zi-qi1, SHI Jie1, ZHOU Jin-ge1. Determination of Mn, Co, Ni in Ternary Cathode Materials With
Homologous Correction EDXRF Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3436-3444. |
| [5] |
HUANG Li, MA Rui-jun*, CHEN Yu*, CAI Xiang, YAN Zhen-feng, TANG Hao, LI Yan-fen. Experimental Study on Rapid Detection of Various Organophosphorus Pesticides in Water by UV-Vis Spectroscopy and Parallel Factor Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3452-3460. |
| [6] |
LI Zhong-bing1, 2, JIANG Chuan-dong2, LIANG Hai-bo3, DUAN Hong-ming2, PANG Wei2. Rough and Fine Selection Strategy Binary Gray Wolf Optimization
Algorithm for Infrared Spectral Feature Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3067-3074. |
| [7] |
SUN Cheng-yu1, JIAO Long1*, YAN Na-ying1, YAN Chun-hua1, QU Le2, ZHANG Sheng-rui3, MA Ling1. Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3098-3104. |
| [8] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
| [9] |
KONG De-ming1, LIU Ya-ru1, DU Ya-xin2, CUI Yao-yao2. Oil Film Thickness Detection Based on IRF-IVSO Wavelength Optimization Combined With LIF Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2811-2817. |
| [10] |
ZHAO Yu-wen1, ZHANG Ze-shuai1, ZHU Xiao-ying1, WANG Hai-xia1, 2*, LI Zheng1, 2, LU Hong-wei3, XI Meng3. Application Strategies of Surface-Enhanced Raman Spectroscopy in Simultaneous Detection of Multiple Pathogens[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2012-2018. |
| [11] |
LI Chang-ming1, CHEN An-min2*, GAO Xun3*, JIN Ming-xing2. Spatially Resolved Laser-Induced Plasma Spectroscopy Under Different Sample Temperatures[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2032-2036. |
| [12] |
ZHAO Yang1, ZHANG Lei2, 3*, CHENG Nian-kai4, YIN Wang-bao2, 3*, HOU Jia-jia5, BAI Cheng-hua1. Research on Space-Time Evolutionary Mechanisms of Species Distribution in Laser Induced Binary Plasma[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2067-2073. |
| [13] |
WANG Bin1, 2, ZHENG Shao-feng2, GAN Jiu-lin1, LIU Shu3, LI Wei-cai2, YANG Zhong-min1, SONG Wu-yuan4*. Plastic Reference Material (PRM) Combined With Partial Least Square (PLS) in Laser-Induced Breakdown Spectroscopy (LIBS) in the Field of Quantitative Elemental Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2124-2131. |
| [14] |
CHENG Xiao-xiang1, WU Na2, LIU Wei2*, WANG Ke-qing2, LI Chen-yuan1, CHEN Kun-long1, LI Yan-xiang1*. Research on Quantitative Model of Corrosion Products of Iron Artefacts Based on Raman Spectroscopic Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2166-2173. |
| [15] |
HU Meng-ying1, 2, ZHANG Peng-peng1, 2, LIU Bin1, 2, DU Xue-miao1, 2, ZHANG Ling-huo1, 2, XU Jin-li1, 2*, BAI Jin-feng1, 2. Determination of Si, Al, Fe, K in Soil by High Pressure Pelletised Sample and Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2174-2180. |
|
|
|
|
