Determination of copper-based mineral species by laser induced breakdown spectroscopy and chemometric methods
The direct identification of mineral species in raw rocks was assessed using laser induced breakdown spectroscopy (LIBS). A total of 162 sulfide rocks with mineralogical relevance in the copper industry were analyzed. These contained bornite (Cu5FeS4), chalcocite (Cu2S), chalcopyrite (CuFeS2), covellite (CuS), enargite (Cu3AsS4), molybdenite (MoS2), and pyrite (FeS2). Samples were collected from different mining locations to account for sample variability. Multivariate methods such as principal component analysis (PCA) and pattern recognition as soft independent modelling of class analogy (SIMCA), partial least square discriminant analysis (PLS-DA), K-nearest neighbor (KNN) and artificial neural networks (ANN) were compared. Sensitivity and robustness tests performed on the LIBS data show that ANN achieves a total of 100% in both. In contrast, SIMCA, KNN and PLS-DA achieve a sensitivity average of 97.6%, 95.1% and 88.1% and robustness of 97.7%, 100% and 98.8%, respectively. The correct identification of very similar species in terms of their elemental composition such as bornite/chalcopyrite and chalcocite/covellite was achieved.