|
|
|
|
|
|
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.
|
Received: 2022-08-08
Accepted: 2022-12-05
|
|
Corresponding Authors:
SONG You-gui
E-mail: syg@ieecas.cn
|
|
[1] Chen J, Li G J. Science China (Earth Sciences), 2011, 54(9): 1279.
[2] Liang M Y, Guo Z T. Kahmann A J, et al. Geochemistry Geophysics Geosystems, 2013, 10(4): Q04004.
[3] Fairchild I J, Treble P C. Quaternary Science Reviews, 2009, 28(5-6): 449.
[4] Butler O T, Cairns W R L, Cook J M, et al. Journal of Analytical Atomic Spectrometry, 2013, 28(2): 177.
[5] Peng J, Liu F, Zhou F, et al. Trends in Analytical Chemistry, 2016, 85: 260.
[6] De Morais C P, Nicolodelli G, Mitsuyuki M C, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2021, 177: 106066.
[7] Cáceres J, Pelascini F, Motto-Ros V, et al. Scientific Reports, 2017, 7(1): 5080.
[8] LI Han-ying, CHENG Hai, WANG Jian, et al(李瀚瑛,程 海,王 健,等). Quaternary Sciences(第四纪研究), 2018, 38(6): 1549.
[9] WU Jie, LI Chuang-kai, CHEN Wen-jun, et al(吴 杰,李创锴,陈文骏,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2022, 42(3): 795.
[10] SHU Rong, XU Wei-ming,FU Zhong-liang, et al(舒 嵘,徐卫明,付中梁,等). Journal of Deep Space Exploration(深空探测), 2018, 5(5): 450.
[11] Costa V C, Ferreira S, Santos L N, et al. Journal of Applied Spectroscopy, 2020, 87(2): 378.
[12] Guo G M, Niu G H, Shi Q, et al. Analytical Methods, 2019, 11(23): 3006.
[13] Feng Z Q, Zhang D C, Wang B W, et al. Plasma Science and Technology, 2020, 22(7): 074012.
[14] Cong Z B, Sun L X, Xin Y, et al. Journal of Computer and Communications, 2013, 1(7): 14.
[15] Rühlmann M, Büchele D, Ostermann M, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2018, 146: 115.
[16] Zhang W, Zhou R, Yang P, et al. Talanta, 2019, 198(6): 93.
[17] Rao C R, Wu Y. Journal of Statistical Planning & Inference, 2005, 128(1): 231.
[18] Andrade J M, Cristoforetti G, Legnaioli S, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2010, 65(8): 658.
|
[1] |
ZHANG Zhi1, GUO Xin-yu1, HANG Yu-hua2, QIU Yan1, WU Jian1*, SUN Hao1, ZHOU Ying1, LI Jing-hui1, MEI Jin-na2, LIAO Kai-xing2. Measurement of Chlorine Distribution in Concrete Based on Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 632-640. |
[2] |
LIU Hong-yang1, 2, KONG De-guo1, 2*, LUO Hua-ping1, 2, GAO Feng1, 2, WANG Cong-ying1, 2. Physical and Chemical Indexes Were Determined Based on Multispectral Image Angle Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 649-655. |
[3] |
ZHANG Mei-ling, CHEN Yong-jie, WANG Min-juan, LI Min-zan, ZHENG Li-hua*. A Hyperspectral Deep Learning Model for Predicting Anthocyanin
Content in Purple Leaf Lettuce[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 865-871. |
[4] |
LIU Jia1, 2, GUO Fei-fei2, YU Lei2, CUI Fei-peng2, ZHAO Ying2, HAN Bing2, SHEN Xue-jing1, 2, WANG Hai-zhou1, 2*. Quantitative Characterization of Components in Neodymium Iron Boron Permanent Magnets by Laser Induced Breakdown Spectroscopy (LIBS)[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 141-147. |
[5] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[6] |
LIU Hao-dong1, 2, JIANG Xi-quan1, 2, NIU Hao1, 2, LIU Yu-bo1, LI Hui2, LIU Yuan2, Wei Zhang2, LI Lu-yan1, CHEN Ting1,ZHAO Yan-jie1*,NI Jia-sheng2*. Quantitative Analysis of Ethanol Based on Laser Raman Spectroscopy Normalization Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3820-3825. |
[7] |
YANG Wen-feng1, LIN De-hui1, CAO Yu2, QIAN Zi-ran1, LI Shao-long1, ZHU De-hua2, LI Guo1, ZHANG Sai1. Study on LIBS Online Monitoring of Aircraft Skin Laser Layered Paint Removal Based on PCA-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3891-3898. |
[8] |
LIN Hong-jian1, ZHAI Juan1*, LAI Wan-chang1, ZENG Chen-hao1, 2, ZHAO Zi-qi1, SHI Jie1, ZHOU Jin-ge1. Determination of Mn, Co, Ni in Ternary Cathode Materials With
Homologous Correction EDXRF Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3436-3444. |
[9] |
HUANG Li, MA Rui-jun*, CHEN Yu*, CAI Xiang, YAN Zhen-feng, TANG Hao, LI Yan-fen. Experimental Study on Rapid Detection of Various Organophosphorus Pesticides in Water by UV-Vis Spectroscopy and Parallel Factor Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3452-3460. |
[10] |
LI Zhong-bing1, 2, JIANG Chuan-dong2, LIANG Hai-bo3, DUAN Hong-ming2, PANG Wei2. Rough and Fine Selection Strategy Binary Gray Wolf Optimization
Algorithm for Infrared Spectral Feature Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3067-3074. |
[11] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[12] |
SUN Cheng-yu1, JIAO Long1*, YAN Na-ying1, YAN Chun-hua1, QU Le2, ZHANG Sheng-rui3, MA Ling1. Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3098-3104. |
[13] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
[14] |
WU Yong-qing1, 2, TANG Na1, HUANG Lu-yao1, CUI Yu-tong1, ZHANG Bo1, GUO Bo-li1, ZHANG Ying-quan1*. Model Construction for Detecting Water Absorption in Wheat Flour Using Vis-NIR Spectroscopy and Combined With Multivariate Statistical #br#
Analyses[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2825-2831. |
[15] |
KONG De-ming1, LIU Ya-ru1, DU Ya-xin2, CUI Yao-yao2. Oil Film Thickness Detection Based on IRF-IVSO Wavelength Optimization Combined With LIF Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2811-2817. |
|
|
|
|