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| Quantitative Analysis of Li in Lithium Ores Based on Laser-Induced Breakdown Spectroscopy |
| FU Hong-bo1, WU Bian1, WANG Hua-dong1, ZHANG Meng-yang1, 2, ZHANG Zhi-rong1, 2* |
1. Anhui Provincial Key Laboratory of Photonics Devices and Materials, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Sciences, Chinese Academy of Sciences, Hefei 230031，China
2. University of Science and Technology of China, Hefei 230026, China
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Abstract Lithium has excellent physical and chemical properties, so it plays an important role in military, battery, special alloy, controlled thermonuclear reactions and other fields. The existing lithium ore analysis methods are mainly off-line methods such as atomic absorption spectrophotometry, inductively coupled plasma mass spectrometry or atomic emission spectrometry based on acid decomposition. Laser-induced breakdown spectroscopy (LIBS) is an atomic emission spectroscopy method without sample preparation and is suitable for low atomic number elements (including lithium). The plasma emission spectra of 11 lithium ore composition analysis reference materials were collected experimentally using LIBS technology. The characteristic peaks of Li were observed near 610.35 and 670.78nm respectively. However, due to the overlap of spectral lines, univariate linear regression cannot be used for modeling. Based on the standardization of full-spectrum integral intensity, partial least squares regression (PLSR) and support vector regression based on principal component analysis (PCA + SVR) are used to model the lithium content in lithium ore reference materials. The relevant parameters of the calibration model are determined by the root to mean square error of the cross-validator (RMSECV). The results show that compared with PCA + SVR calibration model, the determination coefficient (R2) of PLSR is larger, and the calibration root mean square error (RMSEC) is small, but the prediction root mean square error (RMSEP) is much larger than RMSEC, and there is an overfitting phenomenon. On the other hand, the RMSEP and mean relative error (MRE) calculated by PCA + SVR are smaller than PLSR, so we think that PCA + SVR model has good adaptability. This work proves that LIBS technology can analyse Li content in lithium ore and is expected to be applied to the in-situ online quantitative analysis of lithium ore on a conveyor belt.
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Received: 2021-10-14
Accepted: 2022-02-24
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Corresponding Authors:
ZHANG Zhi-rong
E-mail: zhangzr@aiofm.ac.cn
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