Automated ore microscopy based on multispectral measurements of specular reflectance. I – A comparative study of some supervised classification techniques

• Published in: Minerals engineering 2020-01, Vol.146, p.106136, Article 106136
• Main Authors: López-Benito, Alfredo, Catalina, Juan Carlos, Alarcón, David, Grunwald, Úrsula, Romero, Paulo, Castroviejo, Ricardo
• Format: Article
• Language: English
• Subjects: AMCO System; Automated microscopy; Discriminant analysis; Image analysis; Specular reflectance; VNIR
• ISSN: 0892-6875; 1872-9444
• DOI: 10.1016/j.mineng.2019.106136

•Multispectral specular reflectance of 40 ores was measured with the new AMCO system.•The performance of these data for automated ore identification was analyzed.•Four similarity metrics were tested for supervised classification of the spectra.•Success rate exceeded 99% for Mahalanobis Distance or Linear Discriminant Analysis.•Results show Optical Microscopy can be a reliable tool for automated ore mineralogy. Automated mineralogy, including quantitative compositional and textural information, is a requirement for an efficient ore processing, and is comprised as an important input for geometallurgical planning. Classical ore microscopy is seen by many potential users as an outdated, time consuming, tool. Thus, SEM-based systems are often the choice for those who can afford them, in spite of their evident limitations for some minerals (e.g. for iron oxide ores). However, automated and quantitative mineral characterisation of metallic ores is also possible with optical (reflected light) microscopes, attaining similar performance at a much lower price than SEM systems, as shown by the AMCO (Automated Microscopic Characterisation of Ores) prototype. This system relies on the measurement of multispectral specular reflectance, R, on polished ore sections, to achieve automated identification of the ore species (reflectance is computed from grey levels of digital images acquired by automated scanning of the sample). The performance of this approach, supported by a multispectral reflectance database covering the VNIR range (370–1000 nm) built with the AMCO System, is analysed in this paper, comparing the reliability of different classification methods to achieve ore identification. The work outlined in this article focuses on checking the actual behaviour of four classification techniques, based respectively on spectral angle mapper, euclidean distance, Mahalanobis distance and linear discriminant analysis. The tests carried out reveal that the last two techniques are powerful tools to determine to which mineral corresponds a pixel based on its reflectance spectrum. The obtained results prove that automated multispectral optical microscopy is a reliable tool for mineral characterisation of common/industrial ores, with few exceptions (distinction of cassiterite and chromite, or sphalerite and wolframite). For optimisation of its performance, the multispectral information may be complemented with some additional criteria, such as paragenesis or type of deposit (e.g.