Quantification of modal mineralogy in drill-core samples is crucial for understanding the geology and metal deportment in a mining operation. This study assesses conventional procedures to quantify modal mineralogy, that includes an initial drill-core logging, followed by petrographic descriptions and SEM-based automated mineralogy analyses performed in selected regions of interest, against a novel approach using laser-induced breakdown spectroscopy (LIBS). Our proposed methodology aims to quantify the modal mineralogy directly in a drill-core sample, avoiding previous stages of selection and preparation of samples. The novelty of our methodology lies in the simultaneous selection of spectral signals corresponding to a group of elements that are interrelated within a mineral species. The resulting signal combination is strongly correlated with a mineral found in the sample. Our proof of concept combines previously described mineralogy with a detailed spectroscopic and principal component analysis. The selected spectral signals are defined as “mineralogical patterns”, which are processed using supervised chemometrics methods, such as artificial neural networks, to enable an automated mineral classification. We implemented our workflow in three molybdenite-bearing drill-core samples, yielding results comparable to operational characterization, based on petrographic studies, and validated by QEMSCAN analyses, for a suite of ore and gangue minerals, including molybdenite, pyrite, hematite/magnetite, quartz, and aluminosilicates. In brief, we demonstrate how the LIBS-ANN technique can perform automated mineral quantification directly in selected drill-core regions of interest, minimizing previous sample preparation and without expert judgment.

Laser-induced breakdown spectroscopy (LIBS) is expanded for rapid determination of key mineral species in copper ores.

The mineralogical analyses of copper concentrates are very important not only for technical reasons, to monitor and control the smelting processes, but also for commercial purposes, since copper concentrates are sold as an intermediate product. In order to analyze copper concentrate samples in real-time it is necessary to develop fast and efficient measuring methods. This work proposes a simple strategy to combine the information provided by laser-induced breakdown spectroscopy (LIBS) and hyperspectral imaging (HSI) to quantify several mineral species compositions in pellet samples. Since both methods provide complementary information low-level and mid-level data fusion strategies are proposed to combine their measurements and improve the predictions. A data-set of LIBS and HSI obtained from 77 samples of copper concentrates is used for regression tests. A nonlinear model, artificial neural network (ANN), is proposed to approximate the relationship between the mineralogical concentration and the spectral information. In order to reduce the number of inputs of the regression model and improve its generalization capabilities variable selection is performed for LIBS and HSI. The results obtained with mid-level data fusion are encouraging and outperform the ones obtained by using solely the individual sources. Further work is underway to take into account the spatial variability of the minerals in the sample and the detection of minor elements.

The trading price and taxes of copper-concentrate exports depend on the chemical composition. Valuable elements such as Cu, Ag, and Mo increase the price while the presence of As reduces it. The quantification of these elements by laser induced breakdown spectroscopy (LIBS) in mineral ores with varied concentrations spanning from ppm to percentage is challenging. Several factors such as matrix effects, signal fluctuations (laser energy fluctuations, plasma instability, sample inhomogeneity), and self-absorption effects can contribute to analytical errors. Although advanced chemometric methods like artificial neural networks (ANN) can account for these limitations, tuning too many parameters may lead to overfitting of the results. Hence, to avoid overfitting, meaningful information should be fed to the model. In this work, a systematic procedure for getting meaningful LIBS spectral data i.e. "judicious data processing" is proposed. It consists of spectra normalization, feature selection and stratified data division. First, the normalization is done by using an internal standard (ISSN) considering emission lines (Al, Ca, Fe, Zn, and Si) of the main components of the matrix. Second, the feature selection is performed by identifying linearly correlated wavelengths (LCW) with the concentration of each target element. Third, stratified data division in the ANN regression is implemented. This approach, involving LIBS-ANN regression, is applied for multi-elemental quantification of valuable (Cu, Ag, Mo) and penalized (As) elements in copper ores. We compared the ANN models, considering features selection by LCW and prior knowledge spectral lines (PKSL), which are usually utilized in the literature. In terms of analytical figures, the root mean square error of predictions (RMSEP) using LCW improved for Cu (1.45% to 1.04%), Ag (8.5 mg.kg(-1) to 6.0 mg.kg(-1)), and As (0.16% to 0.06%), while it remained unchanged for Mo (0.04%). These improvements can be attributed to the decrease of spectral interferences, caused by the selection of the least affected lines and the compensation of signal fluctuations by spectral normalization.

Coupling HSI and μ-LIBS for elemental and mineralogical imaging in rocks. Elemental and mineral distribution with micrometric spatial resolution. μ-LIBS was expanded to a new field of molecular imaging.

The direct identification of mineral species in raw rocks was performed using laser induced breakdown spectroscopy (LIBS).

The reflection of light on mineral samples have been widely used to obtain information concerning their composition. In particular, visible and near-infrared reflectance spectrum have offered an inexpensive way to obtain information about their mineralogical composition. In this work, near-infrared hyperspectral reflective images of several mineral samples are obtained and analyzed. The average reflective spectrum of Chalcopyrite (CuFeS2), Pyrite (FeS2), Chalcocite (Cu2S), Covellite (CuS), and Slag (FeO-SiO2) packed into pellets were obtained using a near-infrared hyperspectral camera. In order to analyze copper concentrates, a K-Nearest Neighbor classifier was trained to identify its main components. A 10 fold cross validation approach was used to certify the validity of the classifier. The trained classifier provided the mineralogical spatial distribution of the different components in a concentrate sample. An automatic system controlling all the acquisition and image processing stages provides analysis of the concentrate samples. Further work is underway to include additional minerals and to improve implementation issues such as signal filtering. This is the first step towards the design of a low cost system to provide relevant information about the concentrates feeding copper smelters.