Estimation of Mineralogical and Elemental Composition in Copper Concentrates Using Hyperspectral Imaging and Support Vector Machines

Diffuse reflectance spectroscopy is a non-destructive and low-cost technique capable of capturing a large amount of spectral information relevant to mineral analysis. This study evaluates the feasibility of using such spectral data, complemented with reference measurements obtained through QEMSCAN analysis, to develop robust models for predicting the mineralogical and elemental composition of minerals in copper concentrates. Average spectra were acquired from various types of samples mostly a mixture of pyrite and chalcopyrite originating from three different locations to generate mineralogical models for chalcopyrite (%CuFeS₂) and pyrite (%FeS₂), as well as elemental models to estimate %Cu, %Fe, and %S contents. The spectral data were preprocessed using techniques such as Savitzky–Golay smoothing (Sav-gol) and multiplicative scatter correction (MSC) to enhance spectral quality prior to modeling. A regression model based on partial least squares and support vector machines (PLS-SVR) was subsequently trained. The training strategy considered 60% of the data for calibration, 20% for validation, and 20% for prediction. The results yielded coefficients of determination (R²) greater than 0.8 in the prediction phase for %Cu, %Pyrite, and %Chalcopyrite, with root mean square errors (RMSE) of 1.1592 for %Cu, 1.6547 for %Pyrite, and 2.4276 for %Chalcopyrite, suggesting that this methodology holds potential for estimating different types of compositions. Future work will focus on expanding the dataset, improving implementation, and exploring data fusion strategies.