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 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.