Despite significant recent advancements in the sensor technologies, the use of sensors for raw material characterization in the mining industry remains limited. The aim of the present study was to assess the utility of applying the mid-wave infrared (MWIR) reflectance data acquired by the use of a handheld Fourier-transform infrared spectrometer (FTIR), combined with partial least squares-discriminant analysis (PLS-DA), for the characterization of a polymetallic sulphide ore deposit. In achieving the study objectives, focus was given to the MWIR portion of the FTIR dataset, as it is the least explored region of the infrared spectrum in mineral characterization studies. Three datasets—covering different wavelength ranges—were generated from the FTIR spectral data, namely the full FTIR range (2.5–15 µm), MWIR (2.5–7 µm) and long-wave infrared (LWIR: 7–15 µm), in order to investigate the associated information level of each defined wavelength region separately. Design of experiment was developed to determine the optimal data filtering techniques. Using the processed data and PLS-DA, a series of calibration and prediction models were developed for ore and waste materials separately. As the models applied to the MWIR data showed a successful classification rate of 86.3% for sulphide ore–waste discrimination, similarly using the full spectral FTIR dataset, a correct classification rate of 89.5% was achieved. This indicates that MWIR spectral range includes informative signals that are sufficient for classifying the material into ore or waste. The proposed approach could be extended for automating the sulphide ore–waste discrimination process, thus greatly benefiting marginally economical mining operations.
- Material discrimination
- Polymetallic sulphide ore