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Minerals 2017, 7(10), 193;

Near-Infrared Spectroscopy of Limestone Ore for CaO Estimation under Dry and Wet Conditions

Seoul National University Research Institute of Energy and Resources, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Korea Polar Research Institute, 26 Songdomirae-ro, Yeonsu-gu, Incheon 21990, Korea
Department of Energy Systems Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Author to whom correspondence should be addressed.
Received: 16 September 2017 / Revised: 2 October 2017 / Accepted: 11 October 2017 / Published: 13 October 2017
(This article belongs to the Special Issue Geological Modelling)
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Quantitative analysis of CaO in limestone mining is mandatory, not only for ore exploration, but also for grade control. A partial least squares regression (PLSR) CaO estimation technique was developed for limestone mining. The proposed near-infrared spectroscopy (NIR)-based method uses reflectance spectra of the rock sample surface in the visible to short-wave infrared wavelength regions (350–2500 nm (4000–28,571 cm−1)) without the need to crush and pulverize the rock samples. The root mean square (RMS) error of CaO estimation using limestone ore fragment was 1.2%. The CaO content estimated by the PLSR method was used to predict average CaO content of composite samples with a sample size of 15, which resulted in an RMS error of 0.3%. The prediction accuracy with moisture on sample surfaces was also examined to find out if the NIR-based method showed a similar RMS error. Results suggest that the NIR technique can be used as a rapid assaying method in limestone workings with or without the presence of groundwater. View Full-Text
Keywords: NIR spectroscopy; partial least squares regression; limestone; moisture effect NIR spectroscopy; partial least squares regression; limestone; moisture effect

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Oh, S.; Hyun, C.-U.; Park, H.-D. Near-Infrared Spectroscopy of Limestone Ore for CaO Estimation under Dry and Wet Conditions. Minerals 2017, 7, 193.

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