Application of Laser-Induced Breakdown Spectroscopy in Quantitative
Analysis of Sediment Elements
FU Xiao-fen1, SONG You-gui1, 2*, ZHANG Ming-yu3, FENG Zhong-qi4, ZHANG Da-cheng4, LIU Hui-fang1
1. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
2. CAS Center for Excellence in Quaternary Science and Global Change, Xi'an 710061, China
3. Xi'an Institute for Innovative Earth Environment Research, Xi'an 710061, China
4. School of Physics and Optoelectronic Engineering, Xidian University, Xi'an 710071, China
Abstract：Laser-induced breakdown spectroscopy can quickly measure the content or composition of various elements in samples and is widely used in the testing and analysis of environmental samples. However, its application to analysis of multiple elements in geological samples is rarely reported. This study took the drill-core Quaternary Lake sediments of Qinghai Lake and national standard soil samples as the research objects. The original spectra were preprocessed by Savitzky-Golay convolution smoothing and standard normal variable transformation, and through univariate calibration analysis as well as partial least squares regression algorithm to quantitatively analyze nine elements of Na, Ca, Mg, Si, Al, Fe, Mn, Sr and Ba in Qinghai Lake sediment samples. The results of cross-validation were used as the criteria for optimizing the parameters of the PLSR model, and the quantitative accuracy and stability of the PLSR models were evaluated by the coefficient of prediction determination (R2), root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and residual predictive deviation, respectively. The results show that the PLSR algorithm significantly improves the quantitative effect of traditional univariate analysis; the coefficients of determination for prediction are 0.94, 0.94, 0.98, 0.94, 0.97, 0.84, 0.89, 0.98 and 0.76, and the relative analysis errors are 2.74, 2.35, 3.27, 2.97, 3.56, 1.68, 1.54, 4.18 and 0.75. Combined with the results of cross-validation root mean square error and prediction root mean square error, it can be seen that LIBS technology combined with the PLSR algorithm has high prediction accuracy for Na, Ca, Mg, Si, Al and Sr elements. However, the quantitative effects of Fe, Mn and Ba elements are not very satisfactory, indicating that the PLSR algorithm has certain limitations and applicability in the prediction accuracy. In order to further explore the reliability of the LIBS technique is applied to index test of geochemical elements, this paper compared the predicted content ratio of LIBS with the reference content ratio. The variation trend of the two curves is consistent, which verifies the feasibility and effectiveness of LIBS technology applied to sediment element geochemistry. It provides a reliable analytical method for element quantification in sediment samples and also provide new technologies and ideas for the reconstruction of paleoclimate and paleoenvironment.
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