@article{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 = {Minerals},
  volume    = {13},
  journal   = {Minerals},
  number    = {1},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2075-163X},
  doi       = {10.3390/min13010113},
  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}
}
@article{WojcikBrinkmannZduneketal.2020,
  author    = {Wojcik, Michal and Brinkmann, Pia and Zdunek, Rafał and Riebe, Daniel and Beitz, Toralf and Merk, Sven and Cieslik, Katarzyna and Mory, David and Antonczak, Arkadiusz},
  title     = {Classification of copper minerals by handheld laser-induced breakdown spectroscopy and nonnegative tensor factorisation},
  series = {Sensors},
  volume    = {20},
  journal   = {Sensors},
  number    = {18},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1424-8220},
  doi       = {10.3390/s20185152},
  pages     = {17},
  year      = {2020},
  abstract  = {Laser-induced breakdown spectroscopy (LIBS) analysers are becoming increasingly common for material classification purposes. However, to achieve good classification accuracy, mostly noncompact units are used based on their stability and reproducibility. In addition, computational algorithms that require significant hardware resources are commonly applied. For performing measurement campaigns in hard-to-access environments, such as mining sites, there is a need for compact, portable, or even handheld devices capable of reaching high measurement accuracy. The optics and hardware of small (i.e., handheld) devices are limited by space and power consumption and require a compromise of the achievable spectral quality. As long as the size of such a device is a major constraint, the software is the primary field for improvement. In this study, we propose a novel combination of handheld LIBS with non-negative tensor factorisation to investigate its classification capabilities of copper minerals. The proposed approach is based on the extraction of source spectra for each mineral (with the use of tensor methods) and their labelling based on the percentage contribution within the dataset. These latent spectra are then used in a regression model for validation purposes. The application of such an approach leads to an increase in the classification score by approximately 5\% compared to that obtained using commonly used classifiers such as support vector machines, linear discriminant analysis, and the k-nearest neighbours algorithm.},
  language  = {en}
}