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Discrimination of uranium ore concentrates by chemometric data analysis to support provenance assessment for nuclear forensics applications

Abstract of the journal article published in the Journal of Radioanalytical and Nuclear Chemistry
May 19, 2018

Josette El haddad, Aissa Harhira, Alain Blouin, Mohamad Sabsabi
National Research Council Canada
Slobodan Jovanovic, Tara Kell, Ali El-Jaby
Canadian Nuclear Safety Commission


This paper presents work undertaken as part of the Nuclear Material Signature and Provenance Assessment Capability Development Project (NMS/PAC). The NMS/PAC is a whole-of-government Research & Development initiative led by the CNSC aimed at developing, enhancing and expanding Canada’s radioactive and nuclear material metrology and data analytics capabilities to support provenance assessment functions for nuclear forensics operations. NMS/PAC partners include: National Research Council, Atomic Energy of Canada Ltd. /Canadian Nuclear Laboratories and the University of Ottawa.

The NMS/PAC is supported in part by the Canadian Safety and Security Program (CSSP), which is led by Defence Research and Development Canada’s Centre for Security Science, in partnership with Public Safety Canada.

The CSSP is a federally-funded program to strengthen Canada’s ability to anticipate, prevent/mitigate, prepare for, respond to, and recover from natural disasters, serious accidents, crime and terrorism through the convergence of science and technology with policy, operations and intelligence.


This work describes a method for the discrimination of uranium ore concentrates (UOCs) to support provenance assessment for nuclear forensics applications using samples representing twenty producers from around the world. The concentrations were measured using inductively coupled plasma mass spectrometry. UOCs were classified using the support vector machine method relying on 61 down to only 18 element concentrations without affecting the accuracy. New features are calculated from combination of elements from the selected 18 elements, and added to the selected elements improve the classification results. Reducing the number leads to the optimization of laboratory measurements of element signatures in support of nuclear safeguards and forensics applications.

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