TY - JOUR A1 - Brinkmann, Pia A1 - Köllner, Nicole A1 - Merk, Sven A1 - Beitz, Toralf A1 - Altenberger, Uwe A1 - Löhmannsröben, Hans-Gerd T1 - Comparison of handheld and echelle spectrometer to assess copper in ores by means of laser-induced breakdown spectroscopy (LIBS) JF - Minerals N2 - 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. KW - LIBS KW - copper-bearing minerals KW - UVR KW - PCA KW - PLSR Y1 - 2023 U6 - https://doi.org/10.3390/min13010113 SN - 2075-163X VL - 13 IS - 1 PB - MDPI CY - Basel ER - TY - GEN A1 - Riebe, Daniel A1 - Erler, Alexander A1 - Brinkmann, Pia A1 - Beitz, Toralf A1 - Löhmannsröben, Hans-Gerd A1 - Gebbers, Robin T1 - Comparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 786 KW - laser-induced breakdown spectroscopy KW - LIBS KW - proximal soil sensing KW - soil nutrients KW - elemental composition Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-440079 SN - 1866-8372 IS - 786 ER - TY - JOUR A1 - Rethfeldt, Nina A1 - Brinkmann, Pia A1 - Riebe, Daniel A1 - Beitz, Toralf A1 - Köllner, Nicole A1 - Altenberger, Uwe A1 - Löhmannsröben, Hans-Gerd T1 - Detection of Rare Earth Elements in Minerals and Soils by Laser-Induced Breakdown Spectroscopy (LIBS) Using Interval PLS JF - Minerals N2 - 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 KW - LIBS KW - rare earth elements KW - minerals KW - PCA KW - iPLS regression Y1 - 2021 U6 - https://doi.org/10.3390/min11121379 SN - 2075-163X VL - 11 SP - 1 EP - 17 PB - MDPI CY - Basel, Schweiz ER - TY - JOUR A1 - Riebe, Daniel A1 - Erler, Alexander A1 - Brinkmann, Pia A1 - Beitz, Toralf A1 - Löhmannsröben, Hans-Gerd A1 - Gebbers, Robin T1 - Comparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture JF - Sensors N2 - 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. KW - laser-induced breakdown spectroscopy KW - LIBS KW - proximal soil sensing KW - soil nutrients KW - elemental composition Y1 - 2019 U6 - https://doi.org/10.3390/s19235244 SN - 1424-8220 VL - 19 IS - 23 PB - MDPI CY - Basel ER - TY - GEN A1 - Rethfeldt, Nina A1 - Brinkmann, Pia A1 - Riebe, Daniel A1 - Beitz, Toralf A1 - Köllner, Nicole A1 - Altenberger, Uwe A1 - Löhmannsröben, Hans-Gerd T1 - Detection of Rare Earth Elements in Minerals and Soils by Laser-Induced Breakdown Spectroscopy (LIBS) Using Interval PLS T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - 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 T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1254 KW - LIBS KW - rare earth elements KW - minerals KW - PCA KW - iPLS regression Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-557469 SN - 1866-8372 SP - 1 EP - 17 PB - Universitätsverlag Potsdam CY - Potsdam ER - TY - THES A1 - Brinkmann, Pia T1 - Laserinduzierte Breakdownspektroskopie zur qualitativen und quantitativen Bestimmung von Elementgehalten in geologischen Proben mittels multivariater Analysemethoden am Beispiel von Kupfer und ausgewählten Seltenen Erden N2 - Ein schonender Umgang mit den Ressourcen und der Umwelt ist wesentlicher Bestandteil des modernen Bergbaus sowie der zukünftigen Versorgung unserer Gesellschaft mit essentiellen Rohstoffen. Die vorliegende Arbeit beschäftigt 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 Rohstofflagerstätten 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 für diese Aufgabe. Ihre Attraktivität machen insbesondere die Möglichkeiten aus, Feldproben vor Ort ohne Probennahme oder ‑vorbereitung messen zu können, aber auch die Detektierbarkeit sämtlicher Elemente des Periodensystems und die Unabhängigkeit 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 Lagerstätte zu ermitteln, werden in dieser Arbeit Kalibrierungs- und Quantifizierungsstrategien evaluiert. Für die Charakterisierung von Matrixeffekten und zur Klassifizierung von Mineralen werden explorative Datenanalysemethoden angewendet. Die spektroskopischen Untersuchungen erfolgen an Böden und Gesteinen sowie an Mineralen, die Kupfer oder Seltene Erdelemente beinhalten und aus verschiedenen Lagerstätten bzw. von unterschiedlichen Agrarflächen stammen. Für die Entwicklung einer Kalibrierungsstrategie wurden sowohl synthetische als auch Feldproben von zwei verschiedenen Agrarflächen 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 berücksichtigen. Der Untersuchung liegen synthetische sowie Feldproben von Kupfermineralen zugrunde als auch solche die Seltene Erdelemente beinhalten. Die Proben stammen aus verschiedenen Lagerstätten und weisen unterschiedliche Begleitmatrices auf. Mittels der explorativen Datenanalyse erfolgte die Charakterisierung dieser Begleitmatrices. Die dafür angewendete Hauptkomponentenanalyse gruppiert Daten anhand von Unterschieden und Regelmäßigkeiten. Dies erlaubt Aussagen über Gemeinsamkeiten und Unterschiede der untersuchten Proben im Bezug auf ihre Herkunft, chemische Zusammensetzung oder lokal bedingte Ausprägungen. 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 Verknüpfung von LIBS und multivariater Datenanalyse bietet die Möglichkeit durch eine Analyse vor Ort auf eine Probennahme und die entsprechende Laboranalytik weitestgehend zu verzichten und kann somit zum Umweltschutz sowie einer Schonung der natürlichen Ressourcen bei der Prospektion und Exploration von neuen Erzgängen und Lagerstätten beitragen. Die Verteilung von Elementgehalten der untersuchten Gebiete ermöglicht zudem einen gezielten Abbau und damit eine effiziente Nutzung der mineralischen Rohstoffe. N2 - The sustainable use of resources and the environment is an important part of modern mining and the supply of our society with essential raw materials in the future. The present work focuses on the development of analytical strategies that address the technical-practical requirements of the mining process through accurate and rapid on-site analysis, thus contributing to the targeted and sustainable use of raw material deposits. The analyses are based on spectroscopic data obtained by laser-induced breakdown spectroscopy (LIBS) and evaluated by multivariate data analysis. LIB spectroscopy is a promising technique for this task. Its advantages are in particular the possibility to measure field samples on site without sample collection or preparation, but also the detectability of all elements of the periodic table and the independence of the state of matter. In combination with multivariate data analysis, rapid data processing can be performed, allowing statements to be made on the qualitative elemental composition of the samples investigated. With the goal of determining the distribution of elemental contents in a deposit, calibration and quantification strategies are evaluated in this work. Exploratory data analysis methods are used to characterize matrix effects and to classify minerals. Spectroscopic studies are performed on soils and rocks as well as on minerals containing copper or rare earth elements originating from different deposits or from different agricultural sites. To develop a calibration strategy, both synthetic and field samples from two different agricultural sites were analyzed using LIBS. Using calcium, iron and magnesium as example analytes, the evaluation of different calibration methods based on univariate and multivariate methods was performed. Basics of the quantification strategies are the multivariate analysis methods of partial least squares regression (PLSR) and interval PLSR (iPLSR), which consider the whole detected spectrum or partial spectra in the analysis. The investigation is based on synthetic and field samples of copper minerals as well as those containing rare earth elements. The samples are from different deposits and have varying accompanying matrices. Exploratory data analysis was used to characterize these accompanying matrices. The principal component analysis used for this purpose groups data on the basis of differences and regularities. This allows conclusions to be drawn about similarities and differences between the samples examined in terms of their origin, chemical composition or locally determined characteristics. Finally, the classification of copper-bearing minerals was based on non-negative tensor factorization. This method was used with the aim of classifying unknown samples based on their properties. The combination method of LIBS and multivariate data analysis offers the possibility to avoid sampling and the corresponding laboratory analysis as far as possible by an on-site analysis and can thus contribute to environmental protection as well as to a conservation of natural resources during the prospection and exploration of new ore veins and deposits. The distribution of element contents of the investigated areas also enables a precise mining and thus an efficient utilization of the mineral raw materials. KW - LIBS KW - laserinduzierte Breakdownspektroskopie KW - Seltene Erdelemente KW - Kupfer KW - PCA KW - PLSR KW - NTF KW - copper KW - LIBS KW - NTF KW - PCA KW - PLSR KW - rare earth elements KW - laser induced breakdown spectroscopy Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-572128 ER - TY - JOUR A1 - Wojcik, Michal A1 - Brinkmann, Pia A1 - Zdunek, Rafał A1 - Riebe, Daniel A1 - Beitz, Toralf A1 - Merk, Sven A1 - Cieslik, Katarzyna A1 - Mory, David A1 - Antonczak, Arkadiusz T1 - Classification of copper minerals by handheld laser-induced breakdown spectroscopy and nonnegative tensor factorisation JF - Sensors N2 - 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. KW - LIBS KW - NTF KW - HALS KW - classification KW - copper minerals Y1 - 2020 U6 - https://doi.org/10.3390/s20185152 SN - 1424-8220 VL - 20 IS - 18 PB - MDPI CY - Basel ER - TY - GEN A1 - Brinkmann, Pia A1 - Köllner, Nicole A1 - Merk, Sven A1 - Beitz, Toralf A1 - Altenberger, Uwe A1 - Löhmannsröben, Hans-Gerd T1 - Comparison of handheld and echelle spectrometer to assess copper in ores by means of laser-induced breakdown spectroscopy (LIBS) T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1311 KW - LIBS KW - copper-bearing minerals KW - UVR KW - PCA KW - PLSR Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-584742 SN - 1866-8372 IS - 1311 ER -