@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}
}
@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}
}
@phdthesis{Brinkmann2022,
  author    = {Brinkmann, Pia},
  title     = {Laserinduzierte Breakdownspektroskopie zur qualitativen und quantitativen Bestimmung von Elementgehalten in geologischen Proben mittels multivariater Analysemethoden am Beispiel von Kupfer und ausgew{\"a}hlten Seltenen Erden},
  doi       = {10.25932/publishup-57212},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-572128},
  school      = {Universit{\"a}t Potsdam},
  pages     = {148},
  year      = {2022},
  abstract  = {Ein schonender Umgang mit den Ressourcen und der Umwelt ist wesentlicher Bestandteil des modernen Bergbaus sowie der zuk{\"u}nftigen Versorgung unserer Gesellschaft mit essentiellen Rohstoffen. Die vorliegende Arbeit besch{\"a}ftigt sich mit der Entwicklung analytischer Strategien, die durch eine exakte und schnelle Vor-Ort-Analyse den technisch-praktischen Anforderungen des Bergbauprozesses gerecht werden und somit zu einer gezielten und nachhaltigen Nutzung von Rohstofflagerst{\"a}tten beitragen. Die Analysen basieren auf den spektroskopischen Daten, die mittels der laserinduzierten Breakdownspektroskopie (LIBS) erhalten und mittels multivariater Datenanalyse ausgewertet werden. Die LIB-Spektroskopie ist eine vielversprechende Technik f{\"u}r diese Aufgabe. Ihre Attraktivit{\"a}t machen insbesondere die M{\"o}glichkeiten aus, Feldproben vor Ort ohne Probennahme oder ‑vorbereitung messen zu k{\"o}nnen, aber auch die Detektierbarkeit s{\"a}mtlicher Elemente des Periodensystems und die Unabh{\"a}ngigkeit vom Aggregatzustand. In Kombination mit multivariater Datenanalyse kann eine schnelle Datenverarbeitung erfolgen, die Aussagen zur qualitativen Elementzusammensetzung der untersuchten Proben erlaubt. Mit dem Ziel die Verteilung der Elementgehalte in einer Lagerst{\"a}tte zu ermitteln, werden in dieser Arbeit Kalibrierungs- und Quantifizierungsstrategien evaluiert. F{\"u}r die Charakterisierung von Matrixeffekten und zur Klassifizierung von Mineralen werden explorative Datenanalysemethoden angewendet. Die spektroskopischen Untersuchungen erfolgen an B{\"o}den und Gesteinen sowie an Mineralen, die Kupfer oder Seltene Erdelemente beinhalten und aus verschiedenen Lagerst{\"a}tten bzw. von unterschiedlichen Agrarfl{\"a}chen stammen. F{\"u}r die Entwicklung einer Kalibrierungsstrategie wurden sowohl synthetische als auch Feldproben von zwei verschiedenen Agrarfl{\"a}chen mittels LIBS analysiert. Anhand der Beispielanalyten Calcium, Eisen und Magnesium erfolgte die auf uni- und multivariaten Methoden beruhende Evaluierung verschiedener Kalibrierungsmethoden. Grundlagen der Quantifizierungsstrategien sind die multivariaten Analysemethoden der partiellen Regression der kleinsten Quadrate (PLSR, von engl.: partial least squares regression) und der Intervall PLSR (iPLSR, von engl.: interval PLSR), die das gesamte detektierte Spektrum oder Teilspektren in der Analyse ber{\"u}cksichtigen. Der Untersuchung liegen synthetische sowie Feldproben von Kupfermineralen zugrunde als auch solche die Seltene Erdelemente beinhalten. Die Proben stammen aus verschiedenen Lagerst{\"a}tten und weisen unterschiedliche Begleitmatrices auf. Mittels der explorativen Datenanalyse erfolgte die Charakterisierung dieser Begleitmatrices. Die daf{\"u}r angewendete Hauptkomponentenanalyse gruppiert Daten anhand von Unterschieden und Regelm{\"a}ßigkeiten. Dies erlaubt Aussagen {\"u}ber Gemeinsamkeiten und Unterschiede der untersuchten Proben im Bezug auf ihre Herkunft, chemische Zusammensetzung oder lokal bedingte Auspr{\"a}gungen. Abschließend erfolgte die Klassifizierung kupferhaltiger Minerale auf Basis der nicht-negativen Tensorfaktorisierung. Diese Methode wurde mit dem Ziel verwendet, unbekannte Proben aufgrund ihrer Eigenschaften in Klassen einzuteilen. Die Verkn{\"u}pfung von LIBS und multivariater Datenanalyse bietet die M{\"o}glichkeit durch eine Analyse vor Ort auf eine Probennahme und die entsprechende Laboranalytik weitestgehend zu verzichten und kann somit zum Umweltschutz sowie einer Schonung der nat{\"u}rlichen Ressourcen bei der Prospektion und Exploration von neuen Erzg{\"a}ngen und Lagerst{\"a}tten beitragen. Die Verteilung von Elementgehalten der untersuchten Gebiete erm{\"o}glicht zudem einen gezielten Abbau und damit eine effiziente Nutzung der mineralischen Rohstoffe.},
  language  = {de}
}
@article{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 = {Minerals},
  volume    = {11},
  journal   = {Minerals},
  publisher = {MDPI},
  address   = {Basel, Schweiz},
  issn      = {2075-163X},
  doi       = {10.3390/min11121379},
  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{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}
}
@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}
}
@misc{RiebeErlerBrinkmannetal.2019,
  author    = {Riebe, Daniel and Erler, Alexander and Brinkmann, Pia and Beitz, Toralf and L{\"o}hmannsr{\"o}ben, Hans-Gerd and Gebbers, Robin},
  title     = {Comparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {786},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-44007},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-440079},
  pages     = {16},
  year      = {2019},
  abstract  = {The lack of soil data, which are relevant, reliable, affordable, immediately available, and sufficiently detailed, is still a significant challenge in precision agriculture. A promising technology for the spatial assessment of the distribution of chemical elements within fields, without sample preparation is laser-induced breakdown spectroscopy (LIBS). Its advantages are contrasted by a strong matrix dependence of the LIBS signal which necessitates careful data evaluation. In this work, different calibration approaches for soil LIBS data are presented. The data were obtained from 139 soil samples collected on two neighboring agricultural fields in a quaternary landscape of northeast Germany with very variable soils. Reference analysis was carried out by inductively coupled plasma optical emission spectroscopy after wet digestion. The major nutrients Ca and Mg and the minor nutrient Fe were investigated. Three calibration strategies were compared. The first method was based on univariate calibration by standard addition using just one soil sample and applying the derived calibration model to the LIBS data of both fields. The second univariate model derived the calibration from the reference analytics of all samples from one field. The prediction is validated by LIBS data of the second field. The third method is a multivariate calibration approach based on partial least squares regression (PLSR). The LIBS spectra of the first field are used for training. Validation was carried out by 20-fold cross-validation using the LIBS data of the first field and independently on the second field data. The second univariate method yielded better calibration and prediction results compared to the first method, since matrix effects were better accounted for. PLSR did not strongly improve the prediction in comparison to the second univariate method.},
  language  = {en}
}
@article{RiebeErlerBrinkmannetal.2019,
  author    = {Riebe, Daniel and Erler, Alexander and Brinkmann, Pia and Beitz, Toralf and L{\"o}hmannsr{\"o}ben, Hans-Gerd and Gebbers, Robin},
  title     = {Comparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture},
  series = {Sensors},
  volume    = {19},
  journal   = {Sensors},
  number    = {23},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1424-8220},
  doi       = {10.3390/s19235244},
  pages     = {16},
  year      = {2019},
  abstract  = {The lack of soil data, which are relevant, reliable, affordable, immediately available, and sufficiently detailed, is still a significant challenge in precision agriculture. A promising technology for the spatial assessment of the distribution of chemical elements within fields, without sample preparation is laser-induced breakdown spectroscopy (LIBS). Its advantages are contrasted by a strong matrix dependence of the LIBS signal which necessitates careful data evaluation. In this work, different calibration approaches for soil LIBS data are presented. The data were obtained from 139 soil samples collected on two neighboring agricultural fields in a quaternary landscape of northeast Germany with very variable soils. Reference analysis was carried out by inductively coupled plasma optical emission spectroscopy after wet digestion. The major nutrients Ca and Mg and the minor nutrient Fe were investigated. Three calibration strategies were compared. The first method was based on univariate calibration by standard addition using just one soil sample and applying the derived calibration model to the LIBS data of both fields. The second univariate model derived the calibration from the reference analytics of all samples from one field. The prediction is validated by LIBS data of the second field. The third method is a multivariate calibration approach based on partial least squares regression (PLSR). The LIBS spectra of the first field are used for training. Validation was carried out by 20-fold cross-validation using the LIBS data of the first field and independently on the second field data. The second univariate method yielded better calibration and prediction results compared to the first method, since matrix effects were better accounted for. PLSR did not strongly improve the prediction in comparison to the second univariate method.},
  language  = {en}
}