Date Published: April 17, 2018
Publisher: Springer Berlin Heidelberg
Author(s): Agnieszka Martyna, Hans-Eike Gäbler, Andreas Bahr, Grzegorz Zadora.
Wolframite has been specified as a ‘conflict mineral’ by a U.S. Government Act, which obliges companies that use these minerals to report their origin. Minerals originating from conflict regions in the Democratic Republic of the Congo shall be excluded from the market as their illegal mining, trading, and taxation are supposed to fuel ongoing violent conflicts. The German Federal Institute for Geosciences and Natural Resources (BGR) developed a geochemical fingerprinting method for wolframite based on laser ablation inductively coupled plasma-mass spectrometry. Concentrations of 46 elements in about 5300 wolframite grains from 64 mines were determined. The issue of verifying the declared origins of the wolframite samples may be framed as a forensic problem by considering two contrasting hypotheses: the examined sample and a sample collected from the declared mine originate from the same mine (H1), and the two samples come from different mines (H2). The solution is found using the likelihood ratio (LR) theory. On account of the multidimensionality, the lack of normal distribution of data within each sample, and the huge within-sample dispersion in relation to the dispersion between samples, the classic LR models had to be modified. Robust principal component analysis and linear discriminant analysis were used to characterize samples. The similarity of two samples was expressed by Kolmogorov-Smirnov distances, which were interpreted in view of H1 and H2 hypotheses within the LR framework. The performance of the models, controlled by the levels of incorrect responses and the empirical cross entropy, demonstrated that the proposed LR models are successful in verifying the authenticity of the wolframite samples.
In the eastern provinces (North Kivu, South Kivu, and Maniema) of the Democratic Republic of the Congo (DRC), ongoing violent conflicts are fuelled by illegal mining, trading, and taxation of natural resources (e.g., tin, tantalum, and tungsten, their ores, and gold). Foreign and local armed groups profit from mining activities and use the revenue from mineral trade to finance their troops [1, 2]. In 2010 the US Congress passed the Dodd-Frank Wall Street Reform and Consumer Protection Act and charged the Securities and Exchange Commission (SEC) to take action to address virtually all of the mandatory rulemaking provisions of the Act. Section 1502 of this Act requires US-listed companies to exercise due diligence on the traceability of so-called “conflict minerals” (coltan, cassiterite, and wolframite mined to obtain Ta, Sn, and W, respectively, and gold) or their derivatives originating from DRC or adjoining countries if these minerals are necessary for the functionality or production of their products . On the one hand, the Dodd-Frank Act intends to reduce income from mineral trade for armed groups, but on the other hand this Act will also have great impact on regular artisanal miners whose livelihood is strongly dependent on mining of these minerals. However, recently a combination of court opinions, regulatory reversals, and legislative proposals have joined to weaken the conflict mineral regulations under Section 1502 . In 2017, the European Parliament and the Council laid down supply chain due diligence obligations for Union importers of tin, tantalum, and tungsten, their ores, and gold originating from conflict-affected and high-risk areas .
The research presented herein addresses the issue of verifying the authenticity of the declared origins of wolframite samples based on their elemental composition determined by LA-ICP-MS. In the case of a database with multivariate data, huge dispersion of the samples, and clearly not-normal distribution of the data, the evaluation of the evidential value can be supported by using hybrid likelihood ratio models that take the best from the chemometric tools and smartly apply the results within the LR framework. The robust PCA and LDA used in this study are applied to efficiently reduce data dimensionality and extract the features that maximally differentiate between samples coming from different mine sites (non-brother samples). A score-based LR model that incorporated similarity metrics like the Kolmogorov-Smirnov distance (KSD) into the likelihood ratio approach was developed to conclude whether a sample in question with a declared origin and a reference sample (truly coming from the declared location) are brother samples or not.