Application of Laser-Induced Breakdown Spectroscopy in Quantitative Analysis of Sediment Elements

被引:0
作者
Fu Xiao-fen [1 ]
Song You-gui [1 ,2 ]
Zhang Ming-yu [3 ]
Feng Zhong-qi [4 ]
Zhang Da-cheng [4 ]
Liu Hui-fang [1 ]
机构
[1] Chinese Acad Sci, Inst Earth Environm, State Key Lab Loess & Quaternary Geol, Xian 710061, Peoples R China
[2] CAS Ctr Excellence Quaternary Sci & Global Change, Xian 710061, Peoples R China
[3] Xian Inst Innovat Earth Environm Res, Xian 710061, Peoples R China
[4] Xidian Univ, Sch Phys & Optoelect Engn, Xian 710071, Peoples R China
关键词
Laser-induced breakdown spectroscopy; Partial least squares regression; Quantitative analysis; Quaternary sediment; Paleoenvironment; REGRESSION; SOILS;
D O I
10.3964/j.issn.1000-0593(2024)03-0641-08
中图分类号
O433 [光谱学];
学科分类号
0703 ; 070302 ;
摘要
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.
引用
收藏
页码:641 / 648
页数:8
相关论文
共 18 条
[1]   Classical univariate calibration and partial least squares for quantitative analysis of brass samples by laser-induced breakdown spectroscopy [J].
Andrade, Jose Manuel ;
Cristoforetti, Gabriele ;
Legnaioli, Stefano ;
Lorenzetti, Giulia ;
Palleschi, Vincenzo ;
Shaltout, Abdallah A. .
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY, 2010, 65 (08) :658-663
[2]   Atomic spectrometry update. Environmental analysis [J].
Butler, Owen T. ;
Cairns, Warren R. L. ;
Cook, Jennifer M. ;
Davidson, Christine M. .
JOURNAL OF ANALYTICAL ATOMIC SPECTROMETRY, 2013, 28 (02) :177-216
[3]   Megapixel multi-elemental imaging by Laser-Induced Breakdown Spectroscopy, a technology with considerable potential for paleoclimate studies [J].
Caceres, J. O. ;
Pelascini, F. ;
Motto-Ros, V. ;
Moncayo, S. ;
Trichard, F. ;
Panczer, G. ;
Marin-Roldan, A. ;
Cruz, J. A. ;
Coronado, I. ;
Martin-Chivelet, J. .
SCIENTIFIC REPORTS, 2017, 7
[4]   Geochemical studies on the source region of Asian dust [J].
Chen Jun ;
Li GaoJun .
SCIENCE CHINA-EARTH SCIENCES, 2011, 54 (09) :1279-1301
[5]  
Cong Z., 2013, J COMPUT COMMUN, V1, P14, DOI DOI 10.4236/JCC.2013.17004
[6]   Qualitative and Quantitative Analysis of Soils Using Laser-Induced Breakdown Spectroscopy and Chemometrics Tools [J].
Costa, V. C. ;
Ferreira, S. dos Santos ;
Santos, L. N. ;
Speranca, M. A. ;
da Silva, C. Santos ;
Sodre, G. A. ;
Pereira-Filho, E. R. .
JOURNAL OF APPLIED SPECTROSCOPY, 2020, 87 (02) :378-386
[7]   Optimization of laser-induced breakdown spectroscopy parameters from the design of experiments for multi-element qualitative analysis in river sediment [J].
de Morais, Carla Pereira ;
Nicolodelli, Gustavo ;
Mitsuyuki, Milene Corso ;
Mounier, Stephane ;
Pereira Milori, Debora Marcondes Bastos .
SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY, 2021, 177
[8]   Trace elements in speleothems as recorders of environmental change [J].
Fairchild, Ian J. ;
Treble, Pauline C. .
QUATERNARY SCIENCE REVIEWS, 2009, 28 (5-6) :449-468
[9]   The classification of plants by laser-induced breakdown spectroscopy based on two chemometric methods [J].
Feng, Zhongqi ;
Zhang, Dacheng ;
Wang, Bowen ;
Ding, Jie ;
Liu, Xuyang ;
Zhu, Jiangfeng .
PLASMA SCIENCE & TECHNOLOGY, 2020, 22 (07)
[10]   Multi-element quantitative analysis of soils by laser induced breakdown spectroscopy (LIBS) coupled with univariate and multivariate regression methods [J].
Guo, Guangmeng ;
Niu, Guanghui ;
Shi, Qi ;
Lin, Qingyu ;
Tian, Di ;
Duan, Yixiang .
ANALYTICAL METHODS, 2019, 11 (23) :3006-3013