He Laboratory of Applied Microscopy and Image Analysis of the Polytechnic University of Madrid, integrated into RedLab, Laboratory Network of the Community of Madrid (rla207), and located in the ETS Mining and energy engineer, has advanced equipment in the fields of Transmission and Reflection Optical Microscopy, Petrography, Mineralogy, Image Analysis and Microthermometry of Fluid Inclusions. Research stone and construction materials, minerals and metallic ores. It develops applications to problems of mineral genesis, metallogeny, mining exploration and geometallurgy or mineralurgy and its environmental implications, as well as various research projects, whether from competitive public calls or financed by the industry. The completion of doctoral theses, supervised doctoral work, master's theses and final year projects is also part of their usual tasks. He is currently working on the development of an expert system for the automated recognition and characterization of metallic ores and on various industrial and research applications. Being integrated with the Mineral Resources Research Group of the Polytechnic University of Madrid (GIRMI_UPM), easily accesses laboratory facilities and international collaborating networks, such as IGME or DESIR Network (22 institutions in Europe and America).

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 largely depend 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 latest 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 Polytechnic University of 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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