Trace Element Analysis of Rough Diamond by LA‐ICP‐MS: A Case of Source Discrimination?*^,†

Abstract

Current profiling of rough diamond source is performed using different physical and/or morphological techniques that require strong knowledge and experience in the field. More recently, chemical impurities have been used to discriminate diamond source and with the advance of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) empirical profiling of rough diamonds is possible to some extent. In this study, we present a LA-ICP-MS methodology that we developed for analyzing ultra-trace element impurities in rough diamond for origin determination ("profiling"). Diamonds from two sources were analyzed by LA-ICP-MS and were statistically classified by accepted methods. For the two diamond populations analyzed in this study, binomial logistic regression produced a better overall correct classification than linear discriminant analysis. The results suggest that an anticipated matrix match reference material would improve the robustness of our methodology for forensic applications.

Trace Element Analysis of Minerals in Magmatic-Hydrothermal Ores by Laser Ablation Inductively-Coupled Plasma Mass Spectrometry: Approaches and Opportunities

“…Published applications include successful attempts to fingerprint so-called 'conflict diamonds' [150,151], columbite-tantalite and other sought-after ores (e.g., [152,153]). Potential can also be recognised in areas such as uranium concentrates.…”

Analytical Proof of Origin for Raw Materials

“…The first paper providing statistically robust forensic data for diamond discrimination by Dalpé et al [45] concluded that much more work is needed to validate the results, which were positive in general. However, all these studies focused on kimberlitic diamonds and avoided alluvial diamonds.…”

Tourmaline as a Tracer of Late-Magmatic to Hydrothermal Fluid Evolution: The World-Class San Rafael Tin (-Copper) Deposit, Peru

“…Discriminant projection analysis is another multivariate statistical technique that aims to classify a high-dimensional dataset into predefined groups known a priori by calculating a linear discriminant function that maximizes ratios between the groups (Izenman, 2008). This method has been used for geological or forensic investigations of trace element or isotopic analyses of minerals (e.g., Dalpé et al, 2010). We have applied DPA to the same element dataset for San Rafael tourmaline (n = 258 spot analyses, 18 variables) as done for PCA, and defined three groups corresponding to the three generations of tourmaline (Tur 1 to Tur 3).…”