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

• Main Authors: Sungchan Oh, Chang-Uk Hyun, Hyeong-Dong Park
• Format: Article
• Language: English
• Published: MDPI AG 2017-10-01
• Series: Minerals
• Subjects: NIR spectroscopy; partial least squares regression; limestone; moisture effect
• Online Access: https://www.mdpi.com/2075-163X/7/10/193

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.