Origin assessment of uranium ores using multivariate statistical method based on their rare-earth elemental parameters

Origin assessment of nuclear materials is the key aim of nuclear forensics. Among the various fingerprints, rare-earth elements (REEs) are regarded as a powerful geological signature in authentication studies as they behave similarly during geologic and mining/milling processes. In this study, the c...

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Main Authors: Xuepeng Shao, Wenting Bu, Youyi Ni, Hailong Wang, Xuemei Liu, Chuting Yang, Fanhua Hao
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2022-09-01
Series:Nuclear Analysis
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Online Access:http://www.sciencedirect.com/science/article/pii/S2773183922000271
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author Xuepeng Shao
Wenting Bu
Youyi Ni
Hailong Wang
Xuemei Liu
Chuting Yang
Fanhua Hao
author_facet Xuepeng Shao
Wenting Bu
Youyi Ni
Hailong Wang
Xuemei Liu
Chuting Yang
Fanhua Hao
author_sort Xuepeng Shao
collection DOAJ
description Origin assessment of nuclear materials is the key aim of nuclear forensics. Among the various fingerprints, rare-earth elements (REEs) are regarded as a powerful geological signature in authentication studies as they behave similarly during geologic and mining/milling processes. In this study, the combination of rare-earth impurities and Nd–Ce isotope ratios were proposed as a novel fingerprint for the origin assessment of uranium ores. A database was established, comprising mass spectrometric measurements of rare-earth elemental parameters of twenty-five samples from seven countries. The efficiencies of different multivariate statistical techniques, including cluster analysis (CA), principal component analysis (PCA) and linear discriminant analysis (LDA), were compared. The results showed that most of uranium ore samples were correctly classified according to geographical origins, and Nd–Ce isotope ratios played a key role in improving the classification. High recognition (100%) and satisfactory predictive ability (90%) of the developed LDA model proved that the proposed method is a powerful tool for tracing unknown uranium ore samples.
format Article
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institution Kabale University
issn 2773-1839
language English
publishDate 2022-09-01
publisher KeAi Communications Co., Ltd.
record_format Article
series Nuclear Analysis
spelling doaj-art-b3e34eb3eaf44e319b4f410848dcd8422025-08-20T03:42:44ZengKeAi Communications Co., Ltd.Nuclear Analysis2773-18392022-09-011310002710.1016/j.nucana.2022.100027Origin assessment of uranium ores using multivariate statistical method based on their rare-earth elemental parametersXuepeng Shao0Wenting Bu1Youyi Ni2Hailong Wang3Xuemei Liu4Chuting Yang5Fanhua Hao6Institute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang, 621999, ChinaCorresponding author.; Institute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang, 621999, ChinaInstitute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang, 621999, ChinaInstitute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang, 621999, ChinaInstitute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang, 621999, ChinaInstitute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang, 621999, ChinaInstitute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, Mianyang, 621999, ChinaOrigin assessment of nuclear materials is the key aim of nuclear forensics. Among the various fingerprints, rare-earth elements (REEs) are regarded as a powerful geological signature in authentication studies as they behave similarly during geologic and mining/milling processes. In this study, the combination of rare-earth impurities and Nd–Ce isotope ratios were proposed as a novel fingerprint for the origin assessment of uranium ores. A database was established, comprising mass spectrometric measurements of rare-earth elemental parameters of twenty-five samples from seven countries. The efficiencies of different multivariate statistical techniques, including cluster analysis (CA), principal component analysis (PCA) and linear discriminant analysis (LDA), were compared. The results showed that most of uranium ore samples were correctly classified according to geographical origins, and Nd–Ce isotope ratios played a key role in improving the classification. High recognition (100%) and satisfactory predictive ability (90%) of the developed LDA model proved that the proposed method is a powerful tool for tracing unknown uranium ore samples.http://www.sciencedirect.com/science/article/pii/S2773183922000271Uranium oreRare-earth elementsNd–Ce isotope RatiosMultivariate statistical methodNuclear forensics
spellingShingle Xuepeng Shao
Wenting Bu
Youyi Ni
Hailong Wang
Xuemei Liu
Chuting Yang
Fanhua Hao
Origin assessment of uranium ores using multivariate statistical method based on their rare-earth elemental parameters
Nuclear Analysis
Uranium ore
Rare-earth elements
Nd–Ce isotope Ratios
Multivariate statistical method
Nuclear forensics
title Origin assessment of uranium ores using multivariate statistical method based on their rare-earth elemental parameters
title_full Origin assessment of uranium ores using multivariate statistical method based on their rare-earth elemental parameters
title_fullStr Origin assessment of uranium ores using multivariate statistical method based on their rare-earth elemental parameters
title_full_unstemmed Origin assessment of uranium ores using multivariate statistical method based on their rare-earth elemental parameters
title_short Origin assessment of uranium ores using multivariate statistical method based on their rare-earth elemental parameters
title_sort origin assessment of uranium ores using multivariate statistical method based on their rare earth elemental parameters
topic Uranium ore
Rare-earth elements
Nd–Ce isotope Ratios
Multivariate statistical method
Nuclear forensics
url http://www.sciencedirect.com/science/article/pii/S2773183922000271
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