| 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] |
FENG Rui-jie1, CHEN Zheng-guang1, 2*, YI Shu-juan3. Identification of Corn Varieties Based on Bayesian Optimization SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1698-1703. |
| [2] |
MIAO Shu-guang1, SHAO Dan1*, LIU Zhong-yu2, 3, FAN Qiang1, LI Su-wen1, DING En-jie2, 3. Study on Coal-Rock Identification Method Based on Terahertz
Time-Domain Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1755-1760. |
| [3] |
TIAN Xue1, CHE Qian1, YAN Wei-min1, OU Quan-hong1, SHI You-ming2, LIU Gang1*. Discrimination of Millet Varieties and Producing Areas Based on Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1841-1847. |
| [4] |
WANG Ling-ling1, 2, 3, WANG Bo1, 2, 3, XIONG Feng1, 2, 3, YANG Lu-cun1, 2, LI Jing-jing4, XIAO Yuan-ming1, 2, 3, ZHOU Guo-ying1, 2*. A Comparative Study of Inorganic Elements in Cultivativing Astragalus Membranaceus From Different Habitats[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1407-1412. |
| [5] |
TAN Yang1, WU Xiao-hong2, 3*, WU Bin4, SHEN Yan-jun1, LIU Jin-mao1. Qualitative Analysis of Pesticide Residues on Chinese Cabbage Based on GK Improved Possibilistic C-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1465-1470. |
| [6] |
ZHANG Tian-liang, ZHANG Dong-xing, CUI Tao, YANG Li*, XIE Chun-ji, DU Zhao-hui, ZHONG Xiang-jun. Identification of Early Lodging Resistance of Maize by Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1229-1234. |
| [7] |
YAO Shan1, ZHANG Xuan-ling1, CAI Yu-xin1, HE Lian-qiong1, LI Jia-tong1, WANG Xiao-long1, LIU Ying1, 2*. Study on Distribution Characteristics of Different Nitrogen and
Phosphorus Fractions by Spectrophotometry in Baiyangdian
Lake and Source Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1306-1312. |
| [8] |
CAO Qiu-hong, LIN Hong-mei, ZHOU Wei, LI Zhao-xin, ZHANG Tong-jun, HUANG Hai-qing, LI Xue-min, LI De-hua*. Water Quality Analysis Based on Terahertz Attenuated Total Reflection Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 31-37. |
| [9] |
ZHANG Xin-xin1, LI Shang-ke1, LI Pao1, 2*, SHAN Yang2, JIANG Li-wen1, LIU Xia1. A Nondestructive Identification Method of Producing Regions of Citrus Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3695-3700. |
| [10] |
WU Ye-lan1, CHEN Yi-yu1, LIAN Xiao-qin1, LIAO Yu2, GAO Chao1, GUAN Hui-ning1, YU Chong-chong1. Study on the Identification Method of Citrus Leaves Based on Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3837-3843. |
| [11] |
OUYANG Ai-guo, WAN Qi-ming, LI Xiong, XIONG Zhi-yi, WANG Shun, LIAO Qi-cheng. Research on Rich Borer Detection Methods Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3844-3850. |
| [12] |
LIN Hong-mei1, CAO Qiu-hong1, ZHANG Tong-jun1, LI Zhao-xin1, HUANG Hai-qing1, LI Xue-min1, WU Bin2, ZHANG Qing-jian3, LÜ Xin-min4, LI De-hua1*. Identification of Nephrite and Imitations Based on Terahertz Time-Domain Spectroscopy and Pattern Recognition[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3352-3356. |
| [13] |
ZHANG Wei-fang1, 2, FAN Ke-feng3, LEI Jing-wei1, 2*, JI Liang1, 2. Infrared Fingerprint and Multivariate Statistical Analysis of Rehmannia Glutinosa[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3392-3398. |
| [14] |
MENG Fan, LIU Yang*, WANG Huan, YAN Qi-cai. Research and Implementation of High-Performance Wavemeter Based on Principle Component Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3625-3631. |
| [15] |
XU Yu-ting1, SUN Hao-ran2, GAO Xun1*, GUO Kai-min3*, LIN Jing-quan1. Identification of Pork Parts Based on LIBS Technology Combined With PCA-SVM Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3572-3576. |
|
|
|
|
