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Automated Quantitative Mapping of Ore Minerals by Multispectral Reflected-light Microscopy

Published online by Cambridge University Press:  22 July 2022

Juan Carlos Catalina ,
Úrsula Grunwald ,
David Alarcón  and
Ricardo Castroviejo
Juan Carlos Catalina*
Affiliation:
Multispectral Microscopic Technologies S.L. (MMT), Madrid, Spain Laboratorio de Microscopía Aplicada (LMA), Universidad Politécnica de Madrid, Madrid, Spain
Úrsula Grunwald
Affiliation:
Multispectral Microscopic Technologies S.L. (MMT), Madrid, Spain Laboratorio de Microscopía Aplicada (LMA), Universidad Politécnica de Madrid, Madrid, Spain
David Alarcón
Affiliation:
Multispectral Microscopic Technologies S.L. (MMT), Madrid, Spain
Ricardo Castroviejo
Affiliation:
Multispectral Microscopic Technologies S.L. (MMT), Madrid, Spain Laboratorio de Microscopía Aplicada (LMA), Universidad Politécnica de Madrid, Madrid, Spain
*
*Corresponding author: [email protected]

Abstract

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An innovative multispectral reflected-light microscopy system is able to automatically identify ore minerals by measuring their specular reflectance with non-polarized light in a number of spectral bands, and comparing the values obtained with a reference database. In this way it can provide quantitative mineralogical mapping comparable to that obtained from automated mineralogy systems based on SEM-EDS (Scanning Electron Microscopy with Energy-Dispersive X-Ray Spectroscopy), at a fraction of the cost and with less stringent environmental and operational requirements.

The new system, called AMCO (for Automated Microscopic Characterization of Ores), consists of an instrument and two proprietary software applications: amcoCapture, to acquire multispectral images of a polished section prepared from an ore sample, and amcoAnalysis, to process these images and extract different types of mineralogical information from them.

Type
Quantitative and Qualitative Mapping of Materials
Information
Microscopy and Microanalysis , Volume 28 , Supplement S1 , August 2022 , pp. 634 - 637
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Copyright
Copyright © Microscopy Society of America 2022

References

REFERENCES

Grunwald-Romera, U. et al. A reliable method for the automated distinction of quartz gangue and epoxy resin with reflected light microscopy and its application to digital image analysis. Proceedings of the 15th SGA Biennial Meeting (2019).Google Scholar
López-Benito, A. et al. Automated ore microscopy based on multispectral measurements of specular reflectance. I - A comparative study of some supervised classification techniques. Minerals Engineering, Volume 146 (2020), doi:10.1016/j.mineng.2019.106136CrossRefGoogle Scholar
Catalina, J.C. et al. Automated Characterization of Metal Ores by Multispectral Reflected Light Microscopy. Proceedings of Procemin-GEOMET 2021 (17th International Conference on Mineral Processing and Geometallurgy) (2021), p. 29–36.Google Scholar
The authors gratefully acknowledge funding by EIT RawMaterials (EIT project no. 15039), under the Horizon 2020 Framework Program. Further support has been received from EIT RawMaterials CLC South through projects under the Booster Call for start-ups, scale-ups and SMEs in response to the COVID-19 crisis (No 19458-CLCS-13) and the Start-Up & SME Booster Call 2021 (No 15099-SCLC-2021-6). Supported by EIT RawMaterials and funded by the European Union.Google Scholar