The Applied Microscopy and Digital Image Analysis Laboratory (LMA) of the Universidad Politécnica de Madrid (UPM) is located at the Madrid School of Mines and integrated into the RedLab (Laboratory network of the Madrid Region). The LMA is fully equipped with advanced systems in the fields of Optical microscopy (both transmitted and reflected), Petrography, Mineragraphy, Image Analysis and Fluid Inclusion Microthermometry. Research in the fields of rocks and minerals, metallic ores and aggregates is carried out in the LMA along with the development of applications to address issues related to geology, ore genesis, metallogeny, mining exploration, geometallurgy and its environmental implications, all in the frame of a wide variety of research projects with public and/or private foundation. PhD thesis and research, master thesis and degree final projects are frequently carried out in the LMA. Currently, a system is being developed for the automated identification and characterisation of ore minerals for industrial and research applications. On the other hand, its collaboration with the UPM Mineral Resources Research Group (GIRMI-UPM) allows for a quick access to other laboratories facilities and equipments and to international networks collaborating with GIRMI-UPM, such as IGME (Geological Survey of Spain) or DESIR network (formed by 22 European and American institutions).

Reflected light microscopy has been the main method of ore characterization throughout most of the second half of the 20th century, but its fate in the future may depend largely on the ability to increase its performance through reliable quantification and automation. The series of Quantitative Data Files edited by the IMA / COM, particularly the last version, QDF3 (Criddle & Stanley, 1993), represents a milestone in the compilation of reliable spectral information on ore reflectances in visible light, making it possible to aim at automated microscopic identification of ores based on quantitative data. A joint effort by researchers from the Universidad Politécnica de Madrid (UPM), AITEMÍN, Ruhr Universitaet Bochum (RUB), and Université de Liège (ULg) has allowed to collect visible and near-infra-red (VNIR) spectral data from ores in a systematic way for their automatic identification.

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