|
|
|
|
|
|
| PSO-LSSVM Improves the Accuracy of LIBS Quantitative Analysis |
| LIN Xiao-mei1, WANG Xiao-meng1, HUANG Yu-tao1*, LIN Jing-jun2* |
1. Department of Electronics and Electrical Engineering, Changchun University of Technology, Changchun 130012, China
2. Department of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun 130012, China |
|
|
|
|
Abstract Aiming at the problem that the quantitative analysis of soil is greatly affected by the matrix effect and the accuracy of the quantitative analysis of LIBS is not good. The particle swarm algorithm is used to optimize the LSSVM to improve the accuracy of the model. Pb Ⅰ 405.78 nm and Cr Ⅰ 425.44 nm was selected as the analysis lines for analysis. Collect the characteristic spectra of twelve samples with different concentrations. The LSSVM calibration model has a low degree of fitting and cannot meet the experimental requirements. The performance of the model needs to be improved. Use particle swarm optimization to optimize the model parameter penalty coefficient γ and kernel function parameter g of LSSVM to obtain the best combination of γ and g. The Pb element is (8 096.8, 138.865 7), and the Cr element is (4 908.6, 393.563 5). Compared with LSSVM, the accuracy of the PSO-LSSVM calibration model is higher. The R2 of Pb and Cr elements is increased to 0.982 8 and 0.985 0, and the fitting effect is significantly improved. The root means square error of the training set of Pb and Cr elements decreased from 0.026 0 Wt% and 0.027 2 Wt% to 0.022 4 Wt% and 0.019 1 Wt%, and the root means square error of the prediction set was reduced from 0.101 8 Wt% and 0.078 8 Wt% to 0.045 8 Wt% and 0.042 0 Wt%, the stability of the model is further improved. It shows that the PSO-LSSVM algorithm can better reduce the influence of the soil matrix effect and self-absorption effect, and improve the accuracy and stability of the analysis results.
|
|
Received: 2020-10-29
Accepted: 2021-02-11
|
|
|
|
Corresponding Authors:
HUANG Yu-tao, LIN Jing-jun
E-mail: 1004201519@qq.com；1124270941@qq.com
|
|
[1] Ilhardt P D, Nuñez Jamie R, Denis E H, et al. Soil Biology and Biochemistry, 2019, 131: 119.
[2] Kim G, Yoon Y J, Kim H A, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2017, 134: 17.
[3] Villas-Boas P R, Romano R A, Marco Aurélio de Menezes Franco, et al. Geoderma, 2016, 263: 195.
[4] WANG Ping, LI Dui-yuan(汪 萍，李队员). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(4): 1197.
[5] De Morais C P, Barros A I, Júnior, Dário Santos, et al. Microchemical Journal, 2017, 134: 370.
[6] MENG De-shuo, ZHAO Nan-jing, MA Ming-jun, et al(孟德硕, 赵南京, 马明俊, 等). Journal of Optoelectronics·Laser(光电子·激光), 2015, 244(10): 1984.
[7] ZOU Xiao-heng, HAO Zhong-qi, YI Rong-xing, et al(邹孝恒，郝中骐，易荣兴，等). Chinese Journal of Analytical Chemistry(分析化学)，2017，2(43)：181.
[8] Lv Chengxu, Wang Bo, Jiang Xunpeng, et al. Plasma Science and Technology, 2019, 21(3): 112.
[9] Yang L, Yang S, Li S, et al. Knowledge-Based Systems, 2015, 79(May): 80.
[10] Zendehboudi, Alireza. Energy Conversion & Management, 2016, 127: 245.
[11] Torkaman, Mohammad, Safari, et al. Journal of Natural Gas Science & Engineering, 2015, 24: 228.
[12] Li X, Wu J. Advances in Engineering Research, 2016, 4(1): 1456.
[13] XU Jun, LU Hai-yan, SHI Gui-juan(许 君，鲁海燕，石桂娟）. Journal of Computer Applications(计算机应用), 2015, 35(3): 668.
[14] Robledo M A, Sobral H, Garcia V A. Spectrochimica Acta Part B: Atomic Spectroscopy, 2018, 144(6): 7.
[15] Adrian A M, et al. Ksce Journal of Civil Engineering, 2015, 19(3): 1. |
| [1] |
WANG Gan-lin1, LIU Qian1, LI Ding-ming1, YANG Su-liang1*, TIAN Guo-xin1, 2*. Quantitative Analysis of NO-3，SO2-4，ClO-4 With Water as Internal Standard by Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1855-1861. |
| [2] |
YU Zhi-rong, HONG Ming-jian*. Near-Infrared Spectral Quantitative Analysis Network Based on Grouped Fully Connection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1735-1740. |
| [3] |
ZHOU Jun1, 2, YANG Yang2, YAO Yao2, LI Zi-wen3, WANG Jian3, HOU Chang-jun1*. Application of Mid-Infrared Spectroscopy in the Analysis of Key Indexes of Strong Flavour Chinese Spirits Base Liquor[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 764-768. |
| [4] |
LI Yuan1, 2, SHI Yao2*, LI Shao-yuan1*, HE Ming-xing3, ZHANG Chen-mu2, LI Qiang2, LI Hui-quan2, 4. Accurate Quantitative Analysis of Valuable Components in Zinc Leaching Residue Based on XRF and RBF Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 490-497. |
| [5] |
FU Juan, MO Jia-mei, YU Yi-song, ZHANG Qing-zong, CHEN Xiao-li, CHEN Pei-li, ZHANG Shao-hong, SU Qiu-cheng*. Experimental Study on the Structure Characteristics of CO2 in Gas Hydrate by Solid-State Nuclear Magnetic Resonance and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 464-469. |
| [6] |
YU Feng-ping1, LIN Jing-jun1*, LIN Xiao-mei1, 3*, LI Lei1,2*. Detection of C Element in Alloy Steel by Double Pulse Laser Induced Breakdown Spectroscopy With a Multivariable GA-BP-ANN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 197-202. |
| [7] |
WANG Xiao-bin1, 2, 3, ZHANG Xi1, GUAN Chen-zhi1, HONG Hua-xiu1, HUANG Shuang-gen2*, ZHAO Chun-jiang3. Quantitative Detection of Ascorbic Acid Additive in Flour Based on Raman Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3765-3770. |
| [8] |
LIU Jia-cheng1, 2, HU Bing-liang1, YU Tao1*, WANG Xue-ji1, DU Jian1, LIU Hong1, LIU Xiao1, HUANG Qi-xing3. Nonlinear Full-Spectrum Quantitative Analysis Algorithm of Complex Water Based on IERT[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3922-3930. |
| [9] |
MA Li1, 2, FAN Xin-li1, 2, ZHANG Shuo1, 2, WANG Wei-feng1, 2, WEI Gao-ming1, 2. Research on CH4 Gas Detection and Temperature Correction Based on TDLAS Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3632-3638. |
| [10] |
WANG Si-yuan1, ZHANG Bao-jun1, WANG Hao1, GOU Si-yu2, LI Yu1, LI Xin-yu1, TAN Ai-ling1, JIANG Tian-jiu2, BI Wei-hong1*. Concentration Monitoring of Paralytic Shellfish Poison Producing Algae Based on Three Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3480-3485. |
| [11] |
YOU Wen1, XIA Yang-peng1, HUANG Yu-tao1, LIN Jing-jun2*, LIN Xiao-mei3*. Research on Selection Method of LIBS Feature Variables Based on CART Regression Tree[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3240-3244. |
| [12] |
HE Ya-xiong1, ZHOU Wen-qi1, KE Chuan2, XU Tao1*, ZHAO Yong1, 2. Review of Laser-Induced Breakdown Spectroscopy in Gas Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2681-2687. |
| [13] |
ZOU Jin-ping1, ZHANG Shuai2, DONG Wen-tao2, ZHANG Hai-liang2*. Application of Hyperspectral Image to Detect the Content of Total Nitrogen in Fish Meat Volatile Base[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2586-2590. |
| [14] |
OUYANG Ai-guo, LIU Hao-chen, CHENG Long, JIANG Xiao-gang, LI Xiong, HU Xuan. Hyperspectral Image Features Combined With Spectral Features Used to Classify the Bruising Time of Peach[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2598-2603. |
| [15] |
XIAO Wen1, CAO Ying-li1,2*, FENG Shuai1, LIU Ya-di1, JIANG Kai-lun1, YU Zheng-xin1, YAN Li1. Detection of Rice Sheath Blight Disease Index Based on Split-Window Gram-Schmidt Transformation and PSO-SVR Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(07): 2181-2187. |
|
|
|
|
