@misc{RethfeldtBrinkmannRiebeetal.2021,
  author    = {Rethfeldt, Nina and Brinkmann, Pia and Riebe, Daniel and Beitz, Toralf and K{\"o}llner, Nicole and Altenberger, Uwe and L{\"o}hmannsr{\"o}ben, Hans-Gerd},
  title     = {Detection of Rare Earth Elements in Minerals and Soils by Laser-Induced Breakdown Spectroscopy (LIBS) Using Interval PLS},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-55746},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-557469},
  pages     = {1 -- 17},
  year      = {2021},
  abstract  = {The numerous applications of rare earth elements (REE) has lead to a growing global demand and to the search for new REE deposits. One promising technique for exploration of these deposits is laser-induced breakdown spectroscopy (LIBS). Among a number of advantages of the technique is the possibility to perform on-site measurements without sample preparation. Since the exploration of a deposit is based on the analysis of various geological compartments of the surrounding area, REE-bearing rock and soil samples were analyzed in this work. The field samples are from three European REE deposits in Sweden and Norway. The focus is on the REE cerium, lanthanum, neodymium and yttrium. Two different approaches of data analysis were used for the evaluation. The first approach is univariate regression (UVR). While this approach was successful for the analysis of synthetic REE samples, the quantitative analysis of field samples from different sites was influenced by matrix effects. Principal component analysis (PCA) can be used to determine the origin of the samples from the three deposits. The second approach is based on multivariate regression methods, in particular interval PLS (iPLS) regression. In comparison to UVR, this method is better suited for the determination of REE contents in heterogeneous field samples. View Full-Text},
  language  = {en}
}
@misc{BrinkmannKoellnerMerketal.2023,
  author    = {Brinkmann, Pia and K{\"o}llner, Nicole and Merk, Sven and Beitz, Toralf and Altenberger, Uwe and L{\"o}hmannsr{\"o}ben, Hans-Gerd},
  title     = {Comparison of handheld and echelle spectrometer to assess copper in ores by means of laser-induced breakdown spectroscopy (LIBS)},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1311},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-58474},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-584742},
  pages     = {19},
  year      = {2023},
  abstract  = {Its properties make copper one of the world's most important functional metals. Numerous megatrends are increasing the demand for copper. This requires the prospection and exploration of new deposits, as well as the monitoring of copper quality in the various production steps. A promising technique to perform these tasks is Laser Induced Breakdown Spectroscopy (LIBS). Its unique feature, among others, is the ability to measure on site without sample collection and preparation. In this work, copper-bearing minerals from two different deposits are studied. The first set of field samples come from a volcanogenic massive sulfide (VMS) deposit, the second part from a stratiform sedimentary copper (SSC) deposit. Different approaches are used to analyze the data. First, univariate regression (UVR) is used. However, due to the strong influence of matrix effects, this is not suitable for the quantitative analysis of copper grades. Second, the multivariate method of partial least squares regression (PLSR) is used, which is more suitable for quantification. In addition, the effects of the surrounding matrices on the LIBS data are characterized by principal component analysis (PCA), alternative regression methods to PLSR are tested and the PLSR calibration is validated using field samples.},
  language  = {en}
}