| 光谱学与光谱分析 |
|
|
|
|
|
| The Research on Matrix Effect and Correction Technology of Rock Sample in In-Situ Energy Dispersive X-Ray Fluorescence Analysis |
| CHENG Feng1, 2, GU Yi1, 2*, GE Liang-quan1, 2, ZHAO Jian-kun1, LI Meng-ting1, ZHANG Ning1 |
1. The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 610059, China
2. Applied Nuclear Techniques in Geosciences Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu 610059, China |
|
|
|
|
Abstract The mineral constituents of the rock sample can be analyzed with in-situ energy dispersive X-ray fluorescence analysis technology (In-situ EDXRF), the matrix effect of rock sample will effects on measurement results. The Monte Carlo simulation method is used to conduct fluorescence analysis spectrum with ideal measurement conditions, which provides analytical data for matrix effect research. The measured spectrum of seventeen kinds rock samples are being simulated, which has the same Cu content. Therefore, the influences with matrix effect of rock sample in in-situ EDXRF take Cu element for example. Based on correlation between Cu Kα X-ray intensity and spectral parameters, considering elements similarity of all kinds rock samples, it is found that the variation the Cu Kα X-ray intensity not only by the control of rock elements composition or rock classification. The matrix effect of rock samples must be classified according correlation between Cu Kα X-ray intensity and spectral parameters. After the matrix effect classification, fifteen kinds of rock samples, which belong to the same matrix effect, can be corrected more effective. Based on principal component analysis of similar matrix effect rock samples, it is found that the scattering background, target element K-series X-ray of X-ray tube and its incoherent scatter intensity can be a good description of Cu Kα X-ray intensity which is affected by rock matrix, thus it can be used to correct the Cu element measurement results. Certainly, this technology can also provide reference for matrix effect correction to other elements in rock.
|
|
Received: 2016-05-31
Accepted: 2016-10-15
|
|
|
|
Corresponding Authors:
GU Yi
E-mail: guyi10@cdut.cn
|
|
[1] JI Ang(吉 昂). Rock and Mineral Analysis(岩矿测试), 2012, 31(3): 383.
[2] JI Ang, ZHUO Shang-jun, LI Guo-hui(吉昂, 卓尚军, 李国会). Energy Dispersive X-ray Fluorescence Spectrum(能量色散X射线荧光光谱). Beijing: Science Press(北京: 科学出版社), 2011.
[3] Steven M Shackley. X-Ray Fluorescence Spectrometry (XRF) in Geoarchaeology. New York: Springer Science+Business Media.
[4] QI Hai-jun, WANG Jian-ying, ZHANG Xue-feng, et al(齐海君, 王建英, 张雪峰, 等). Spectroscopy and Spectral Anaylsis(光谱学与光谱分析), 2015, 35(12): 3510.
[5] GE Liang-quan, ZHANG Ye(葛良全, 章 晔). Journal of Chengdu College of Geology(成都地质学院学报), 1991, 17(4): 118.
[6] Moxtek, Inc. Mobile Miniature X-Ray Source. United States, 2002, Patent No: US6661876B2.
[7] ZHONG Rui-yuan, TENG Yi-jun, KONG Hua, et al(种瑞元, 滕以俊, 孔 华, 等). Rock Classification and Identification(岩石分类命名与鉴定). Shenyang: Liaoning Bureau of Geology and Mineral Resurces(沈阳: 辽宁省地质矿产局), 1984.
[8] A Markowicz. Pramana-J. Phys., 2011, 76(2): 321.
[9] Hunter D B, Bertsch P M. Journal of Radioanalytical and Nuclear Chemistry, 1998, 234: 237.
[10] Pagès-Camagna S, Laval E, Vigears D, et al. Appl. Phys. A, 2010, 100: 671. |
| [1] |
WANG Cai-ling1，ZHANG Jing1，WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
| [2] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
| [3] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [4] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
| [5] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
| [6] |
JIA Zong-chao1, WANG Zi-jian1, LI Xue-ying1, 2*, QIU Hui-min1, HOU Guang-li1, FAN Ping-ping1*. Marine Sediment Particle Size Classification Based on the Fusion of
Principal Component Analysis and Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3075-3080. |
| [7] |
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. |
| [8] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
| [9] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
| [10] |
CAO Qian, MA Xiang-cai, BAI Chun-yan, SU Na, CUI Qing-bin. Research on Multispectral Dimension Reduction Method Based on Weight Function Composed of Spectral Color Difference[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2679-2686. |
| [11] |
ZHANG Zi-hao1, GUO Fei3, 4, WU Kun-ze1, YANG Xin-yu2, XU Zhen1*. Performance Evaluation of the Deep Forest 2021 (DF21) Model in
Retrieving Soil Cadmium Concentration Using Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2638-2643. |
| [12] |
CHEN Wan-jun1, XU Yuan-jie2, LU Zhi-yun3, QI Jin-hua3, WANG Yi-zhi1*. Discriminating Leaf Litters of Six Dominant Tree Species in the Mts. Ailaoshan Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2119-2123. |
| [13] |
WANG Yu-hao1, 2, LIU Jian-guo1, 2, XU Liang2*, DENG Ya-song2, SHEN Xian-chun2, SUN Yong-feng2, XU Han-yang2. Application of Principal Component Analysis in Processing of Time-Resolved Infrared Spectra of Greenhouse Gases[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2313-2318. |
| [14] |
HU Hui-qiang1, WEI Yun-peng1, XU Hua-xing1, ZHANG Lei2, MAO Xiao-bo1*, ZHAO Yun-ping2*. Identification of the Age of Puerariae Thomsonii Radix Based on Hyperspectral Imaging and Principal Component Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1953-1960. |
| [15] |
LIU Yu-juan1, 2, 3 , LIU Yan-da1, 2, 3, SONG Ying1, 2, 3*, ZHU Yang1, 2, 3, MENG Zhao-ling1, 2, 3. Near Infrared Spectroscopic Quantitative Detection and Analysis Method of Methanol Gasoline[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1489-1494. |
|
|
|
|
