Near-infrared (NIR) reflectance spectroscopy has been used to improve the efficiency of processes and quality of products in many industries. Mining and ore processing are under-utilizing the technology and could benefit from expanded application of NIR. NIR spectroscopy combined with chemometric predictive models are now being increasingly used as rapid techniques to quantitatively analyze mineral ores to provide real-time information that is needed for ore processing. While NIR is most commonly applied to analysis of organic materials, many inorganic minerals have significant NIR features that result from variations in their composition, thus providing the opportunity to create predictive models for these minerals.
Near-infrared (NIR) reflectance spectroscopy has been used to improve the efficiency of processes and quality of products in many industries. Mining and ore processing are under-utilizing the technology and could benefit from expanded application of NIR. NIR spectroscopy combined with chemometric predictive models are now being increasingly used as rapid techniques to quantitatively analyze mineral ores to provide real-time information that is needed for ore processing. While NIR is most commonly applied to analysis of organic materials, many inorganic minerals have significant NIR features that result from variations in their composition, thus providing the opportunity to create predictive models for these minerals.
