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

Sensors
Feven DestaJeroen Jansen

Abstract

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 ...Continue Reading

References

Oct 19, 2004·Analytical Chemistry·Rajendrani Mukhopadhyay
Jan 26, 2010·The Analyst·Richard G Brereton, Gavin R Lloyd
Nov 30, 2011·Advances in Neonatal Care : Official Journal of the National Association of Neonatal Nurses·Terri Marin, James Moore
Jun 5, 2014·Cytometry. Part a : the Journal of the International Society for Analytical Cytology·Balázs Szalontai, László Zimányi

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Methods Mentioned

BETA
X-ray
infrared spectroscopy
feature extraction
PCR
PCA
light scattering
scanning electron microscopy

Software Mentioned

Unscrambler
R
PLSR

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