Data Evaluation for Cassiterite and Coltan Fingerprinting
| Autor: | Wilhelm Schink, Hans-Eike Gäbler, Timo Gawronski |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Multivariate statistics
lcsh:QE351-399.2 Traceability Computer science Supply chain Sample (statistics) Coltan engineering.material 010502 geochemistry & geophysics computer.software_genre 01 natural sciences analytical fingerprint cassiterite 0105 earth and related environmental sciences Statistical hypothesis testing lcsh:Mineralogy 010401 analytical chemistry Fingerprint (computing) Cassiterite Geology data evaluation Geotechnical Engineering and Engineering Geology 0104 chemical sciences engineering Data mining coltan computer |
| Zdroj: | Minerals, Vol 10, Iss 926, p 926 (2020) Minerals Volume 10 Issue 10 |
| Popis: | Within due diligence concepts for raw material supply chains, the traceability of a shipment is a major aspect that has to be taken into account. Cassiterite and coltan are two so-called conflict minerals for which traceability systems have been established. To provide additional credibility to document-based traceability systems the German Federal Institute for Geosciences and Natural Resources (BGR) has developed the analytical fingerprint (AFP) for the minerals coltan, cassiterite, and wolframite. AFP is based on the analysis of a sample from a shipment with a declared origin and evaluates whether the declared origin is plausible or not. This is done by comparison to reference samples previously taken at the declared mine site. In addition to the generation of the analytical data, the data evaluation step, with the aim to state whether the declared origin is plausible or not, is of special importance. Two data evaluation approaches named &ldquo Kolmogorov&ndash Smirnov distance (KS-D) approach&rdquo and &ldquo areas ratio approach&rdquo are applied to coltan and cassiterite and result in very low rates of false negative results, which is desired for AFP. The areas ratio approach based on hypothesis testing and a more sophisticated evaluation of the multivariate data structure has some advantages in terms of producing lower rates of false positive results compared to the KS-D approach. |
| Databáze: | OpenAIRE |
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