Calibration curve and support vector regression methods applied for quantification of cement raw meal using laser-induced breakdown spectroscopy

Junwei JIA (贾军伟); Hongbo FU (付洪波); Zongyu HOU (侯宗余); Huadong WANG (王华东); Zhibo NI (倪志波); Fengzhong DONG (董凤忠)

doi:10.1088/2058-6272/aae3e1

Plasma Science and Technology > 2019 > 21(3): 34003-034003. > DOI: 10.1088/2058-6272/aae3e1

Junwei JIA (贾军伟), Hongbo FU (付洪波), Zongyu HOU (侯宗余), Huadong WANG (王华东), Zhibo NI (倪志波), Fengzhong DONG (董凤忠). Calibration curve and support vector regression methods applied for quantification of cement raw meal using laser-induced breakdown spectroscopy[J]. Plasma Science and Technology, 2019, 21(3): 34003-034003. DOI: 10.1088/2058-6272/aae3e1

Citation:

PDF (1006 KB)

Calibration curve and support vector regression methods applied for quantification of cement raw meal using laser-induced breakdown spectroscopy

¹ Anhui Provincial Key Laboratory of Photonic Devices and Materials, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, People’s Republic of China
² University of Science and Technology of China, Hefei 230022, People’s Republic of China
³ State Key Lab of Power Systems, Department of Thermal Engineering, Tsinghua-BP Clean Energy Center, Tsinghua University, Beijing 100084, People’s Republic of China

Funds: This work is supported by National Natural Science Foundation of China (Grant Nos. 61505223, 41775128), the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. Y03RC21124), the External Cooperation Program of Chinese Academy of Sciences (Grant No. GJHZ1726) and the project of China State Key Lab. of Power System (Grant Nos. SKLD18KM11, SKLD18M12).

More Information

Received Date: July 19, 2018

Graphical Abstract

Abstract

Abstract

Laser-induced breakdown spectroscopy (LIBS) is a qualitative and quantitative analytical technique with great potential in the cement industrial analysis. Calibration curve (CC) and support vector regression (SVR) methods coupled with LIBS technology were applied for the quantification of three types of cement raw meal samples to compare their analytical concentration range and the ability to reduce matrix effects, respectively. To reduce the effects of fluctuations of the pulse-to-pulse, the unstable ablation and improve the reproducibility, all of the analysis line intensities were normalized on a per-detector basis. The prediction results of the elements of interest in the three types of samples, Ca, Si, Fe, Al, Mg, Na, K and Ti, were compared with the results of the wet chemical analysis. The average relative error (ARE), relative standard deviation (RSD) and root mean squared error of prediction (RMSEP) were employed to investigate and evaluate the prediction accuracy and stability of the two prediction methods. The maximum average ARE of the CC and SVR methods is 34.62% instead of 6.13%, RSD is 40.89% instead of 7.60% and RMSEP is 1.34% instead of 0.43%. The results show that SVR method can accurately analyze samples within a wider concentration range and reduce the matrix effects, and LIBS coupled with it for a rapid, stable and accurate quantification of different types of cement raw meal samples is promising.
- cement raw meal,
- calibration curves,
- SVR,
- LIBS

FullText(HTML)

References (25)

References

[1]	Lei Y et al 2011 Atmos. Environ. 45 147
[2]	Lemberge P, Van Espen P J and Vrebos B A R 2000 X-Ray Spectrom. 29 297
[3]	Polat R et al 2004 J. Quant. Spectrosc. Radiat. Transfer 83 377
[4]	Chang M T et al 2017 Int. J. Adv. Eng. Sci. Appl. Math. 9 136
[5]	Taefi N, Khalaji M and Tavassoli S H 2010 Cem. Concr. Res. 40 1114
[6]	Wang J G et al 2015 Plasma Sci. Technol. 17 649
[7]	Zhang L et al 2012 Front. Phys. 7 690
[8]	Hu L et al 2015 Plasma Sci. Technol. 17 699
[9]	Zhang S et al 2018 Front. Phys. 13 135201
[10]	Wang Z et al 2012 Front. Phys. 7 708
[11]	Cremers D A and Radziemski L J 2006 Handbook of Laser- Induced Breakdown Spectroscopy (New Jersey: Wiley) (https://doi.org/10.1002/0470093013)
[12]	Gondal M A et al 2009 Spectrosc. Lett. 42 171
[13]	Mansoori A et al 2011 Opt. Lasers Eng. 49 318
[14]	Gehlen C D et al 2009 Spectrochim. Acta B 64 1135
[15]	Yin H L et al 2016 J. Anal. At. Spectrom. 31 2384
[16]	Li Y F et al 2016 Spectrosc. Spectral Anal. 36 1494
[17]	Owolabi T O and Gondal M 2017 J. Anal. At. Spectrom. (https://doi.org/10.1039/C7JA00229G)
[18]	Gu Y H et al 2016 Chin. Phys. Lett. 33 085201
[19]	Zhang T L et al 2015 J. Anal. At. Spectrom. 30 368
[20]	Shi Q et al 2015 J. Anal. At. Spectrom. 30 2384
[21]	Boucher T F et al 2015 Spectrochim. Acta B 107 1
[22]	Barnett W B, Fassel V A and Kniseley R N 1968 Spectrochim. Acta B 23 643
[23]	Fan R E et al 2005 J. Mach. Learn. Res. 6 1889 (http://jmlr. org/papers/v6/fan05a.html)
[24]	Awad M and Khanna R 2015 Support Vector Regression Efficient Learning Machines (Berkeley, CA: Apress) (https://doi.org/10.1007/978-1-4302-5990-9_4)
[25]	Smola A J and Sch?lkopf B 2004 Stat. Comput. 14 199

[1]	Luyun JIANG, Yutong CHEN, Chentao MAO, Jianhui HAN, Anmin CHEN, Jifei YE. Performance optimization of ammonium dinitramide-based liquid propellant in pulsed laser ablation micro-propulsion using LIBS[J]. Plasma Science and Technology, 2025, 27(1): 015503. DOI: 10.1088/2058-6272/ad92f8
[2]	Junwei JIA, Zhifeng LIU, Congyuan PAN, Huaqin XUE. Detection of Al, Mg, Ca, and Zn in copper slag by LIBS combined with calibration curve and PLSR methods[J]. Plasma Science and Technology, 2024, 26(2): 025507. DOI: 10.1088/2058-6272/ad1045
[3]	Yaguang MEI (梅亚光), Shusen CHENG (程树森), Zhongqi HAO (郝中骐), Lianbo GUO (郭连波), Xiangyou LI (李祥友), Xiaoyan ZENG (曾晓雁), Junliang GE (葛军亮). Quantitative analysis of steel and iron by laser-induced breakdown spectroscopy using GA-KELM[J]. Plasma Science and Technology, 2019, 21(3): 34020-034020. DOI: 10.1088/2058-6272/aaf6f3
[4]	Jiajia HOU (侯佳佳), Lei ZHANG (张雷), Yang ZHAO (赵洋), Zhe WANG (王哲), Yong ZHANG (张勇), Weiguang MA (马维光), Lei DONG (董磊), Wangbao YIN (尹王保), Liantuan XIAO (肖连团), Suotang JIA (贾锁堂). Mechanisms and efficient elimination approaches of self-absorption in LIBS[J]. Plasma Science and Technology, 2019, 21(3): 34016-034016. DOI: 10.1088/2058-6272/aaf875
[5]	Xiaomeng LI (李晓萌), Huili LU (陆慧丽), Jianhong YANG (阳建宏), Fu CHANG (常福). Semi-supervised LIBS quantitative analysis method based on co-training regression model with selection of effective unlabeled samples[J]. Plasma Science and Technology, 2019, 21(3): 34015-034015. DOI: 10.1088/2058-6272/aaee14
[6]	Hongbo FU (付洪波), Zhibo NI (倪志波), Huadong WANG (王华东), Junwei JIA (贾军伟), Fengzhong DONG (董凤忠). Accuracy improvement of calibration-free laser-induced breakdown spectroscopy[J]. Plasma Science and Technology, 2019, 21(3): 34001-034001. DOI: 10.1088/2058-6272/aaead6
[7]	Yao JIA (贾尧), Nanjing ZHAO (赵南京), Li FANG (方丽), Mingjun MA (马明俊), Deshuo MENG (孟德硕), Gaofang YIN (殷高方), Jianguo LIU (刘建国), Wenqing LIU (刘文清). Online calibration of laser-induced breakdown spectroscopy for detection of heavy metals in water[J]. Plasma Science and Technology, 2018, 20(9): 95503-095503. DOI: 10.1088/2058-6272/aac42f
[8]	Shuxia ZHAO (赵书霞), Lei ZHANG (张雷), Jiajia HOU (侯佳佳), Yang ZHAO (赵洋), Wangbao YIN (尹王保), Weiguang MA (马维光), Lei DONG (董磊), Liantuan XIAO (肖连团), Suotang JIA (贾锁堂). Accurate quantitative CF-LIBS analysis of both major and minor elements in alloys via iterative correction of plasma temperature and spectral intensity[J]. Plasma Science and Technology, 2018, 20(3): 35502-035502. DOI: 10.1088/2058-6272/aa97ce
[9]	F. MEHARI, M. ROHDE, C. KNIPFER, R. KANAWADE, F. KL¨AMPFL, W. ADLER, N. OETTER, F. STELZLE, M. SCHMIDT. Investigation of Laser Induced Breakdown Spectroscopy (LIBS) for the Differentiation of Nerve and Gland Tissue–A Possible Application for a Laser Surgery Feedback Control Mechanism[J]. Plasma Science and Technology, 2016, 18(6): 654-660. DOI: 10.1088/1009-0630/18/6/12
[10]	WEN Guanhong(温冠宏), SUN Duixiong(孙对兄), SU Maogen(苏茂根), DONG Chenzhong(董晨钟). LIBS Detection of Heavy Metal Elements in Liquid Solutions by Using Wood Pellet as Sample Matrix[J]. Plasma Science and Technology, 2014, 16(6): 598-601. DOI: 10.1088/1009-0630/16/6/11

Cited By

Cited by

Periodical cited type(14)

1.	Zhang, C., Song, W., Lyu, Y. et al. Dual-branch convolutional neural network with attention modules for LIBS-NIRS data fusion in cement composition quantification. Analytica Chimica Acta, 2025. DOI:10.1016/j.aca.2025.343899
2.	Jiang, J., Pang, X., Feng, J. et al. Identification of Antibacterial Components from Compound Sophora Flavescens Extract by Mean Impact Value Based on Support Vector Regression \| [基于 SVR 模型的 MIV 法的复方苦参抗菌成分的辨识研究]. Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)/Journal of Tianjin University Science and Technology, 2025, 58(2): 138-146. DOI:10.11784/tdxbz202401006
3.	Cai, Y., Ma, X., Wang, X. Quantitative analysis of cement raw materials composition by laser-induced breakdown spectroscopy based on iPLS feature band selection. 2025. DOI:10.1145/3704558.3707063
4.	Chang, C., Di Maio, F., Bheemireddy, R. et al. Rapid quality control for recycled coarse aggregates (RCA) streams: Multi-sensor integration for advanced contaminant detection. Computers in Industry, 2025. DOI:10.1016/j.compind.2024.104196
5.	Hao, Z., Liu, K., Lian, Q. et al. Machine learning in laser-induced breakdown spectroscopy: A review. Frontiers of Physics, 2024, 19(6): 62501. DOI:10.1007/s11467-024-1427-2
6.	Cai, Y., Ma, X., Huang, B. et al. LIBS combined with SG-SPXY spectral data pre-processing for cement raw meal composition analysis. Applied Optics, 2024, 63(6): A24-A31. DOI:10.1364/AO.505255
7.	Jia, W., Zhang, Z., Shan, Q. et al. Determination of Molybdenum in Geological Ores by Laser-Induced Breakdown Spectroscopy (LIBS) with Support Vector Machine Regression (SVMR) and Data Preprocessing. Analytical Letters, 2024, 57(13): 2004-2017. DOI:10.1080/00032719.2023.2284216
8.	Zhang, C., Song, W., Hou, Z. et al. Improving quantitative analysis of cement elements in laser-induced breakdown spectroscopy through combining matrix matching with regression. Journal of Analytical Atomic Spectrometry, 2023, 38(12): 2554-2561. DOI:10.1039/d3ja00306j
9.	Luo, X., Chen, R., Kabir, M.H. et al. Fast Detection of Heavy Metal Content in Fritillaria thunbergii by Laser-Induced Breakdown Spectroscopy with PSO-BP and SSA-BP Analysis. Molecules, 2023, 28(8): 3360. DOI:10.3390/molecules28083360
10.	Cabral, J.S., Menegatti, C.R., Nicolodelli, G. Laser-induced breakdown spectroscopy in cementitious materials: A chronological review of cement and concrete from the last 20 years. TrAC - Trends in Analytical Chemistry, 2023. DOI:10.1016/j.trac.2023.116948
11.	Xie, G., Sun, L., Shang, D. et al. Model transfer method based on piecewise direct standardization in laser-induced-breakdown spectroscopy. Applied Optics, 2022, 61(30): 9069-9077. DOI:10.1364/AO.471891
12.	Li, X., Lu, X., Zhang, Y. et al. Effect of the target positions on the rapid identification of aluminum alloys by using filament-induced breakdown spectroscopy combined with machine learning. Chinese Physics B, 2022, 31(5): 054212. DOI:10.1088/1674-1056/ac3810
13.	Wang, G., Sun, L., Wang, W. et al. A feature selection method combined with ridge regression and recursive feature elimination in quantitative analysis of laser induced breakdown spectroscopy. Plasma Science and Technology, 2020, 22(7): 074002. DOI:10.1088/2058-6272/ab76b4
14.	Fu, Y., Hou, Z., Deguchi, Y. et al. From big to strong: Growth of the Asian laser-induced breakdown spectroscopy community. Plasma Science and Technology, 2019, 21(3): 030101. DOI:10.1088/2058-6272/aaf873

Other cited types(0)

Get Citation

PDF

XML

Article views (141) PDF downloads (281) Cited by(14)

Turn off MathJax

Article Contents

Abstract

References

Calibration curve and support vector regression methods applied for quantification of cement raw meal using laser-induced breakdown spectroscopy