Abstract
Growing public interest in getting information on the origin of raw materials used to manufacture goods for daily life has triggered the development of concepts to increase the transparency of raw material supply chains. Analytical proofs of origin (APOs) for raw materials may support those transparency concepts by giving evidence about the origin of a specific raw material shipment. For a variety of raw materials like gemstones, TTT (tantalum, tin, tungsten) minerals, and others, APOs have been developed. The identification of features that distinguish different origins, databases of those features from reliable reference samples, and a data evaluation strategy adopted to the envisaged application scenario are the key aspects of APO methods. Here, an overview is given on APO methods developed for different raw materials and application cases.
Highlights
Analytical proof of origin methods for raw materials are successfully applied under the following assumptions: (1) minerals have measurable compositions/properties which differ depending on their genesis; (2) minerals from an area of enrichment are more closely related to each other than to the same mineral from a second zone of enrichment, e.g., a different ore body
Dixon [36], the majority of samples stored in the South African gold database consist of drillings from doré gold bars, which are the product of mining activity at a single shaft or several shafts, mining the same reef and supplying a smelter on site
Trace element chemistry and isotope ratios are used frequently to determine the geographic origin of ruby and sapphire using binary or ternary discriminating plots or multivariate linear discriminant analysis (LDA) based on various analytical tools like LA-ICP-MS, portable
Summary
Analytical proof of origin methods for raw materials are successfully applied under the following assumptions: (1) minerals have measurable compositions/properties which differ depending on their genesis; (2) minerals from an area of enrichment (orebody) are more closely related to each other than to the same mineral from a second zone of enrichment, e.g., a different ore body. Evaluation tools which use tabulated decision criteria/critical values (e.g., Kolmogorov–Smirnov test, Wilcoxon’s rank sum test, Kruskal–Wallis test) assume that both samples are independent observations that are representative of a single underlying population [11] This assumption is often not met, e.g., if both samples are obtained from different zones of an ore body during the progress of mining operations. Deduced decision criteria which reflect biased sampling conditions may be applied to solve this problem
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