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

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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).

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Received Date: July 19, 2018

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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

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