Material Evidence Analysis and Regional Classification and Identification of Soil Based on X-ray Fluorescence Spectrometry and X-ray Diffraction

JIN Yi; AN Shuai; LIU Xin; SONG Lihua; ZHAO Enhao; MA Jiansheng; ZHANG Zhibin

doi:10.15898/j.ykcs.202403140047

2024 Vol. 43, No. 5

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JIN Yi, AN Shuai, LIU Xin, SONG Lihua, ZHAO Enhao, MA Jiansheng, ZHANG Zhibin. Material Evidence Analysis and Regional Classification and Identification of Soil Based on X-ray Fluorescence Spectrometry and X-ray Diffraction[J]. Rock and Mineral Analysis, 2024, 43(5): 744-754. doi: 10.15898/j.ykcs.202403140047

Citation:

JIN Yi, AN Shuai, LIU Xin, SONG Lihua, ZHAO Enhao, MA Jiansheng, ZHANG Zhibin. Material Evidence Analysis and Regional Classification and Identification of Soil Based on X-ray Fluorescence Spectrometry and X-ray Diffraction[J]. Rock and Mineral Analysis, 2024, 43(5): 744-754. doi: 10.15898/j.ykcs.202403140047

Material Evidence Analysis and Regional Classification and Identification of Soil Based on X-ray Fluorescence Spectrometry and X-ray Diffraction

1.
College of Forensic Science and Technology, Criminal Investigation Police University of China, Shenyang 110035, China
2.
Key Laboratory of Forensic Science, Liaoning Province, Shenyang 110035, China
3.
Shenyang Center of Geological Survey, China Geological Survey, Shenyang 110034, China
4.
Key Laboratory of Black Land Evolution and Ecological Effects, Ministry of Natural Resources, Shenyang 110034, China

More Information

Corresponding author: AN Shuai, Chemical1618@126.com

Received Date: 14 March 2024

Revised Date: 06 July 2024

Accepted Date: 19 July 2024

Publish Date: 30 September 2024

Abstract

Abstract

In the field of forensic science identification, geochemical-related materials such as soils and rocks were important sources of material evidence. In actual case analysis, the information provided by material evidence often pointed to unknown areas. Predicting the source of material evidence was an extremely challenging task when the location of the crime scene was not clear. To address the uncertainty of geochemical material evidence information, a dataset including physicochemical properties and geographic information such as mineral composition, element content, and geographical location was established. By comparing the sample information from the crime scene, the source of the material evidence samples could be quickly determined, providing strong technical and evidentiary support for case investigation. Surface soil samples (0−10cm) were collected from urban areas in Shenyang City, Liaoning Province, and X-ray fluorescence spectrometry (XRF) and X-ray powder diffraction (XRD) were applied to test and analyze 15 elements (SiO₂, Al₂O₃, CaO, K₂O, Na₂O, MgO, TFe₂O₃, Ti, Mn, Ba, P, Zr, Cu, Zn and Pb) and mineral components in the soil material evidence samples. With the help of MapGIS software, element content distribution maps were drawn to explore the characteristics and influencing factors of element distribution in the study area. Principal component analysis (PCA) was used to classify and identify soil samples from three study areas. The results indicated that: (1) through urban geological mapping, accurate and intuitive element content distribution maps can be obtained, allowing court workers to compare the elemental characteristics of soil samples and trace the source of material evidence. (2) The soil material evidence samples from Shenyang were mainly composed of quartz, feldspar, montmorillonite, and illite (88.0%−98.0%). The XRD diffraction bar thermal map facilitated court workers to conduct comparative analysis of large amounts of data. (3) Based on PCA, a dimensionality reduction analysis of 15 elements from three study areas was conducted, and significant regional discrimination of soil samples from the three study areas was achieved within a 95% confidence interval (Group 1: F₁<0, F₂<0; Group 2: F₁>0, F₂>0; Group 3: F₁>0, F₂<0). (4) There were significant differences in chlorite, tremolite, kaolinite, calcite, and dolomite among soil samples from the three study areas, further supporting the accuracy of PCA classification. In summary, the combined application of XRF and XRD technology could be used to effectively distinguish soil material evidence samples from different areas within the city, providing directed research areas for soil material evidence traceability investigation and important clues to narrow down the investigation scope.
- soil material evidence sample /
- X-ray fluorescence spectrometry /
- X-ray powder diffraction /
- principal component analysis

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References

[1]	Xu X, Du C, Ma F, et al. Forensic soil analysis using laser-induced breakdown spectroscopy (LIBS) and Fourier transform infrared total attenuated reflectance spectroscopy (FTIR-ATR): Principles and case studies[J]. Forensic Science International, 2020, 310(5): 110222. doi: 10.1016/j.forsciint.2020.110222 CrossRef Google Scholar
[2]	Scientific American. Science and art: Curious use of the micro-scope[J]. Scientific American, 1856, 11(30): 240−240. Google Scholar
[3]	Norazwani B A, Loong C L, Ab A L. Evaluation of row-wise manipulations for the forensic differentiation of Malaysian soils based on ultra-performance liquid chromatographic profiles[J]. Journal of Analytical Chemistry, 2022, 77(3): 347−360. doi: 10.1134/S1061934822030029 CrossRef Google Scholar
[4]	Omel’yanyuk G G, Kopelchuk N V. Expert forensic environmental and soil investigation in the administration of justice[J]. Moscow University Soil Science Bulletin, 2023, 78(6): 140−148. doi: 10.3103/S0147687423020084 CrossRef Google Scholar
[5]	Foster N R, Martin B, Hoogewerff J, et al. The utility of dust for forensic intelligence: Exploring collection methods and detection limits for environmental DNA, elemental and mineralogical analyses of dust samples[J]. Forensic Science International, 2023, 344(3): 111599. doi: 10.1016/j.forsciint.2023.111599 CrossRef Google Scholar
[6]	Kikkawa H S, Naganuma K, Kumisaka K, et al. Semi-automated scanning electron microscopy energy dispersive X-ray spectrometry forensic analysis of soil samples[J]. Forensic Science International, 2019, 305: 109947. doi: 10.1016/j.forsciint.2019.109947 CrossRef Google Scholar
[7]	Lorna A D. Soil science in the criminal justice system[J]. Encyclopedia of Soils in the Environment (Second Edition), 2023, 3: 521−531. doi: 10.1016/B978-0-12-822974-3.00208-1 CrossRef Google Scholar
[8]	Testoni S A, Prandel L V, Melo V F, et al. Conjunctive use of synchrotron X-ray diffraction and Rietveld refinement in Fe-oxide clays for forensic applications[J]. Journal of Forensic Sciences, 2022, 67(5): 2020−2031. doi: 10.1111/1556-4029.15098 CrossRef Google Scholar
[9]	Murray K, Fitzpatrick R, Doyle R. Development of soil forensic methods and databases from targeted locations in Tasmania to assist Police[C]. 2012. Google Scholar
[10]	Bong W S, Nakai I, Furuya S, et al. Development of heavy mineral and heavy element database of soil sediments in Japan using synchrotron radiation X-ray powder diffraction and high-energy (116keV) X-ray fluorescence analysis[J]. Forensic Science International, 2012, 220(1−3): 33−49. doi: 10.1016/j.forsciint.2012.01.024 CrossRef Google Scholar
[11]	郭洪玲, 王萍, 朱军, 等. 泥土物证XRF元素定量检验数据比对评价方法比较[J]. 刑事技术, 2020, 45(6): 568−574. doi: 10.16467/j.1008-3650.2020.06.004 CrossRef Google Scholar Guo H L, Wang P, Zhu J, et al. Comparison of two statistical methods for evaluating XRF-based elemental quantitative data of soils[J]. Forensic Science and Technology, 2020, 45(6): 568−574. doi: 10.16467/j.1008-3650.2020.06.004 CrossRef Google Scholar
[12]	郭洪玲, 王萍, 朱军, 等. 法庭地质学与泥土物证检验[J]. 刑事技术, 2019, 44(1): 53−59. doi: 10.16467/j.1008-3650.2019.01.012 CrossRef Google Scholar Guo H L, Wang P, Zhu J, et al. Forensic geology and soil examination[J]. Forensic Science and Technology, 2019, 44(1): 53−59. doi: 10.16467/j.1008-3650.2019.01.012 CrossRef Google Scholar
[13]	王黎. 法庭科学泥土物证检验及数据统计方法研究[D]. 北京: 中国人民公安大学, 2020: 16−34. Google Scholar Wang L. The examination of soil evidences and research on data statistical methods in forensic science[D]. Beijing: People’s Public Security University of China, 2020: 16−34. Google Scholar
[14]	闵红, 刘倩, 张金阳, 等. X射线荧光光谱-X射线粉晶衍射-偏光显微镜分析12种产地铜精矿矿物学特征[J]. 岩矿测试, 2021, 40(1): 74−84. doi: 10.15898/j.cnki.11-2131/td.202004020038 CrossRef Google Scholar Min H, Liu Q, Zhang J Y, et al. Study on the mineralogical characteristics of 12 copper concentrates by X-ray fluorescence spectrometry, X-ray powder diffraction and polarization microscope[J]. Rock and Mineral Analysis, 2021, 40(1): 74−84. doi: 10.15898/j.cnki.11-2131/td.202004020038 CrossRef Google Scholar
[15]	吕铷麟, 贾镇, 何洪源, 等. 基于ICP-MS和机器学习的土壤物证溯源研究[J]. 化学研究与应用, 2021, 33(9): 1776−1784. doi: 10.3969/j.issn.1004-1656.2021.09.020 CrossRef Google Scholar Lyu R L, Jia Z, He H Y, et al. Research on soil material evidence traceability based on ICP-MS and machine learning[J]. Chemical Research and Application, 2021, 33(9): 1776−1784. doi: 10.3969/j.issn.1004-1656.2021.09.020 CrossRef Google Scholar
[16]	侯赛文. 基于离子色谱对典型土壤物证分析及溯源研究[D]. 北京: 中国人民公安大学, 2023: 10−11. Google Scholar Hou S W. Analysis and traceability of typical soil evidence based on ion chromatography[D]. Beijing: People’s Public Security University of China, 2023: 10−11. Google Scholar
[17]	李廷栋, 刘勇, 丁孝忠, 等. 中国区域地质研究的十大进展[J]. 地质学报, 2022, 96(5): 1544−1581. doi: 10.19762/j.cnki.dizhixuebao.2022145 CrossRef Google Scholar Li T D, Liu Y, Ding X Z, et al. Ten advances in regional geological research of China in recent years[J]. Acta Geologica Sinica, 2022, 96(5): 1544−1581. doi: 10.19762/j.cnki.dizhixuebao.2022145 CrossRef Google Scholar
[18]	Li Q H, Li C F, Liu L J, et al. Geochemical characteristics of heavy metals of bedrock, soil, and tea in a metamorphic rock area of Guizhou Province, China[J]. Environmental Science and Pollution Research, 2023, 30(3): 7402−7414. doi: 10.1007/s11356-022-22751-0 CrossRef Google Scholar
[19]	谢学锦, 任天祥, 奚小环, 等. 中国区域化探全国扫面计划卅年[J]. 地球学报, 2009, 30(6): 700−716. doi: 10.3321/j.issn:1006-3021.2009.06.003 CrossRef Google Scholar Xie X J, Ren T X, Xi X H, et al. The implementation of the regional geochemistry-national reconnaissance program (RGNR) in China in the past thirty years[J]. Acta Geoscientica Sinica, 2009, 30(6): 700−716. doi: 10.3321/j.issn:1006-3021.2009.06.003 CrossRef Google Scholar
[20]	杨泽, 刘国栋, 戴慧敏, 等. 黑龙江兴凯湖平原土壤硒地球化学特征及富硒土地开发潜力[J]. 地质通报, 2021, 40(10): 1773−1782. doi: 10.12097/j.issn.1671-2552.2021.10.019 CrossRef Google Scholar Yang Z, Liu G D, Dai H M, et al. Selenium geochemistry of soil and development potential of Se-rich soil in Xingkai Lake Plain[J]. Geological Bulletin of China, 2021, 40(10): 1773−1782. doi: 10.12097/j.issn.1671-2552.2021.10.019 CrossRef Google Scholar
[21]	张妍, 赵新雷, 冯雪珍, 等. 河南荥阳市耕地土壤重金属分布特征及来源解析[J]. 岩矿测试, 2024, 43(2): 330−343. doi: 10.15898/j.ykcs.202306300084 CrossRef Google Scholar Zhang Y, Zhao X L, Feng X Z, et al. Distribution characteristics, ecological risks, and source identification of heavy metals in cultivated land in Xingyang City[J]. Rock and Mineral Analysis, 2024, 43(2): 330−343. doi: 10.15898/j.ykcs.202306300084 CrossRef Google Scholar
[22]	Wang L L, Xu J Z, Wang Y, Cheng P, et al. Hg distribution and risk assessment in soil-Bozhou peony system[J]. Environmental Earth Sciences, 2024, 83(6): 1.1−1.9. doi: 10.1007/s12665-024-11492-7 CrossRef Google Scholar
[23]	王磊, 卓小雄, 吴天生, 等. 基于1∶25万和1∶5万土地质量地球化学调查评价的土壤元素累积趋势预测——以广西南宁市西乡塘区为例[J]. 物探与化探, 2023, 47(1): 1−13. doi: 10.11720/wtyht.2023.2613 CrossRef Google Scholar Wang L, Zhuo X X, Wu T S, et al. Prediction of the soil element accumulation trends based on 1∶250000 and 1∶50000 geochemical surveys and assessments of land quality: A case study of Xixiangtang District, Nanning City, Guangxi Zhuang Autonomous Region[J]. Geophysical and Geochemical Exploration, 2023, 47(1): 1−13. doi: 10.11720/wtyht.2023.2613 CrossRef Google Scholar
[24]	李炆颖. 沈阳经济区土地利用要素性冲突作用机理及其缓解路径研究[D]. 沈阳: 沈阳师范大学, 2022: 12−14. Google Scholar Li W Y. The action mechanisms and mitigation pathways of land use elemental conflicts in Shenyang Economic Zone[D]. Shenyang: Shenyang Normal University, 2022: 12−14. Google Scholar
[25]	郑泓. 基于生态安全格局构建的国土空间生态修复策略研究[D]. 沈阳: 沈阳大学, 2022: 9−11. Google Scholar Zheng H. Research on territorial space ecological restoration strategies based on the construction of ecolo-gical security pattern—A case study of Shenyang modern metropolitan[D]. Shenyang: Shenyang University, 2022: 9−11. Google Scholar
[26]	李佳宁. 沈阳市主城区土地利用分类的变化对生态环境质量的影响研究[D]. 沈阳: 沈阳农业大学, 2023: 12−14. Google Scholar Li J N. Study on the impact of land use classification change on eco-environmental quality in the main urban area of Shenyang[D]. Shenyang: Shenyang Agricultural University, 2023: 12−14. Google Scholar
[27]	Liu X, Zhang P J, Liu H, et al. Quantitative phase analysis method of palygorskite based on XRD orientational sample analysis and Rietveld full spectrum fitting[J]. Applied Clay Science, 2024, 248: 107222.1 −107222.10. doi: 10.1016/j.clay.2023.107222 CrossRef Google Scholar
[28]	刘粤惠, 刘平安. X射线衍射分析原理与应用[M]. 北京: 化学工业出版社, 2003: 217−220. Google Scholar Liu E H, Liu P A. Principle and application of X-ray diffraction analysis[M]. Beijing: Press of Chemical Engineering, 2003: 217−220. Google Scholar
[29]	周君龙, 陈宜浩, 李全忠, 等. X射线衍射Rietveld全谱拟合法定量检测滑石混合矿物中滑石含量[J]. 冶金分析, 2023, 43(4): 18−23. doi: 10.13228/j.boyuan.issn1000-7571.011954 CrossRef Google Scholar Zhou J L, Chen Y H, Li Q Z, et al. Quantitative determination of talc in mixed ore by X-ray diffraction Rietveld full spectrum fitting method[J]. Metallurgical Analysis, 2023, 43(4): 18−23. doi: 10.13228/j.boyuan.issn1000-7571.011954 CrossRef Google Scholar
[30]	伍月, 迟广成, 刘欣. X射线粉晶衍射法在变粒岩鉴定与分类中的应用[J]. 岩矿测试, 2020, 39(4): 546−554. doi: 10.15898/j.cnki.11-2131/td.201908050117 CrossRef Google Scholar Wu Y, Chi G C, Liu X. Application of X-ray powder diffraction method in identification and classification of leptite[J]. Rock and Mineral Analysis, 2020, 39(4): 546−554. doi: 10.15898/j.cnki.11-2131/td.201908050117 CrossRef Google Scholar
[31]	中国科学院贵阳地球化学研究所. 矿物X射线粉晶鉴定手册[M]. 北京: 科学出版社, 1978: 117−122. Google Scholar
[32]	石旭飞, 代雅建, 崔健, 等. 沈阳城市地质环境问题研究[J]. 地质与资源, 2017, 26(4): 390−396. doi: 10.3969/j.issn.1671-1947.2017.04.010 CrossRef Google Scholar Shi X F, Dai Y J, Cui J, et al. Study on the geological environment of Shenyang City[J]. Geology and Resources, 2017, 26(4): 390−396. doi: 10.3969/j.issn.1671-1947.2017.04.010 CrossRef Google Scholar
[33]	Neal F, Pascal B, Huang W M, et al. Principal component analysis (PCA) unravels spectral components present in XPS spectra of complex oxide films on iron foil[J]. Applied Surface Science Advances, 2023, 17(8): 100447. doi: 10.1016/j.apsadv.2023.100447 CrossRef Google Scholar
[34]	贾宗潮, 王子鉴, 李雪莹, 等. 主成分分析和连续投影融合的海洋沉积物粒度分类研究[J]. 光谱学与光谱分析, 2023, 43(10): 3075−3080. doi: 10.3964/j.issn.1000-0593(2023)10-3075-06 CrossRef Google Scholar Jia Z C, Wang Z J, Li X Y, et al. 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. doi: 10.3964/j.issn.1000-0593(2023)10-3075-06 CrossRef Google Scholar
[35]	刘凯, 戴慧敏, 刘国栋, 等. 基于主成分聚类法的典型黑土区土壤地球化学分类[J]. 物探与化探, 2022, 46(5): 1132−1140. doi: 10.11720/wtyht.2022.0043 CrossRef Google Scholar Liu K, Dai H M, Liu G D, et al. Geochemical classification of the soil in a typical black soil area using the principal component analysis combined with K-means clustering algorithm[J]. Geophysical and Geochemical Exploration, 2022, 46(5): 1132−1140. doi: 10.11720/wtyht.2022.0043 CrossRef Google Scholar
[36]	王萍, 刘玲利, 郭洪玲, 等. 泥土物证的理化综合检验分析[J]. 刑事技术, 2016, 41(6): 454−458. doi: 10.16467/j.1008-3650.2016.06.006 CrossRef Google Scholar Wang P, Liu L L, Guo H L, et al. Comprehensive analysis of soil evidence with multiple physicochemical methods[J]. Forensic Science and Technology, 2016, 41(6): 454−458. doi: 10.16467/j.1008-3650.2016.06.006 CrossRef Google Scholar