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Open AccessArticle

Fusion of Mid-Wave Infrared and Long-Wave Infrared Reflectance Spectra for Quantitative Analysis of Minerals

1
Resource Engineering Section, Department of Geoscience and Engineering, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands
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Department of Analytical Chemistry: Chemometrics, Faculty of Science, Radboud University, P.O. Box 9010, 6500 GL Nijmegen, The Netherlands
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(5), 1472; https://doi.org/10.3390/s20051472
Received: 16 January 2020 / Revised: 25 February 2020 / Accepted: 4 March 2020 / Published: 7 March 2020
(This article belongs to the Section Optical Sensors)
Accurate quantitative mineralogical data has significant implications in mining operations. However, quantitative analysis of minerals is challenging for most of the sensor outputs. Thus, it requires advances in data analytics. In this work, data fusion approaches for integrating datasets pertaining to the mid-wave infrared (MWIR) and long-wave infrared (LWIR) spectral regions are proposed, aiming to facilitate more accurate prediction of SiO2, Al2O3, and Fe2O3 concentrations in a polymetallic sulphide deposit. Two approaches of low-level data fusion were applied to these datasets. In the first approach, the pre-processed blocks of MWIR and LWIR data were concatenated to form a fused data block. In the second approach, a prior variable selection was performed to extract the most important features from the MWIR and LWIR datasets. The extracted informative features were subsequently concatenated to form a new fused data block. Next, prediction models that link the mineralogical concentrations with the infrared reflectance spectra were developed using partial-least squares regression (PLSR), principal component regression (PCR) and support vector regression (SVR) analytical techniques. These models were applied to the fused data blocks as well as the individual (MWIR and LWIR) data blocks. The obtained results indicate that SiO2, Al2O3, and Fe2O3 mineral concentrations can be successfully predicted using both MWIR and LWIR spectra individually, but the prediction performance greatly improved with data fusion; where the PLSR, PCR, and SVR models provided good and acceptable results. The proposed approach could be extended for online analysis of mineral concentrations in different deposit types. Thus, it would be highly beneficial in mining operations, where indications of mineralogical concentrations can have significant financial implications. View Full-Text
Keywords: MWIR; LWIR; polymetallic sulphide ore; minerals; data fusion; PLSR; PCR; SVM MWIR; LWIR; polymetallic sulphide ore; minerals; data fusion; PLSR; PCR; SVM
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Desta, F.; Buxton, M.; Jansen, J. Fusion of Mid-Wave Infrared and Long-Wave Infrared Reflectance Spectra for Quantitative Analysis of Minerals. Sensors 2020, 20, 1472.

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