Analytical Techniques for Online Mineral Identification

Navid Khajehzadeh

Research output: ThesisDoctoral ThesisCollection of Articles


The exploration and mineral processing phases of mining need advanced measurement and analytical techniques to speed up the process of mineral identification. Rapid mineral identification is necessary for a better process control and efficient use of energy and raw materials. Although the spectroscopic techniques performed in the laboratories provide accurate results, the process of measuring, data preparation and analysis is slow and far from the goal of rapid analysis. Geologists are enthusiastic about the abundance of the minerals as well as the mineral map of the surface of the drill core samples. Therefore, there is a need to develop new techniques enabling rapid mineral identification of the rock and ore drill core samples. Ore beneficiation is yet another process where online mineral identification is required. The control of the flotation processes is strongly relying on the online analysis of elemental contents in the process feed, final product and tailings, as well as in the intermediate material flows inside the process. However, it is the minerals that affect how the ore behaves in the flotation process. Therefore, online analysis of minerals would enable more accurate process control in several flotation applications when compared to the online elemental analysis. This thesis investigates whether the data of the currently available measurement techniques could be utilized to extract mineralogical information. The target is qualitative and quantitative mineral identification with the smallest amount of investment or modification of the instruments. The main results of the thesis show that the spectral integration of the commonly used spectroscopic techniques such as X-ray fluorescence (XRF), Laser-induced fluorescence (LIF), Laser-induced breakdown spectroscopy (LIBS), reflectance spectroscopy and Raman spectroscopy, enables the rapid identification of mineral contents. Advanced statistical techniques such as partial least squares (PLS) provide a means of determining the mineral contents from the available measured spectra.
Original languageEnglish
QualificationDoctor's degree
Awarding Institution
  • Aalto University
  • Visala, Arto, Supervising Professor
  • Zenger, Kai, Thesis Advisor
Print ISBNs978-952-60-8182-3
Electronic ISBNs978-952-60-8183-0
Publication statusPublished - 2018
MoE publication typeG5 Doctoral dissertation (article)


  • mineral identification
  • x-ray fluorescence
  • laser-induced fluorescence
  • laser-induced breakdown spectroscopy
  • reflectance spectroscopy
  • data fusion
  • data modeling
  • partial least squares regression


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