TY - JOUR A1 - Riebe, Daniel A1 - Beitz, Toralf A1 - Dosche, Carsten A1 - Löhmannsröben, Hans-Gerd A1 - Raab, Volker A1 - Raab, Corinna A1 - Unverzagt, Matthias T1 - High-resolution spectrometer using combined dispersive and interferometric wavelength separation for raman and laser-induced Breakdown Spectroscopy (LIBS) JF - Applied spectroscopy : an international journal of spectroscopy ; official publication of the Society for Applied Spectroscopy N2 - In this paper the concept of a compact high-resolution spectrometer based on the combination of dispersive and interferometric elements is presented. Dispersive elements are used to spectrally resolve the light in one direction with coarse resolution (Delta lambda < 0.5 nm), while perpendicular to that direction an etalon provides high spectral resolution (Delta lambda < 50 pm). This concept for two-dimensional spectroscopy has been implemented for the wavelength range lambda = 350-650 nm. Appropriate algorithms for reconstructing spectra from the two-dimensional raw data and for wavelength calibration were established in an analysis software. Potential applications for this new spectrometer are Raman and laser-induced breakdown spectroscopy (LIBS). Resolutions down to 28 pm (routinely 54 pm) could be realized for these applications. KW - Raman spectroscopy KW - Laser-induced breakdown spectroscopy KW - LIBS KW - Fabry-Perot etalon KW - High-resolution spectrometer Y1 - 2014 U6 - https://doi.org/10.1366/13-07426 SN - 0003-7028 SN - 1943-3530 VL - 68 IS - 9 SP - 1030 EP - 1038 PB - Society for Applied Spectroscopy CY - Frederick 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 - 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 - Erler, Alexander A1 - Riebe, Daniel A1 - Beitz, Toralf A1 - Löhmannsröben, Hans-Gerd A1 - Gebbers, Robin T1 - Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR) T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Precision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 815 KW - LIBS KW - lasso KW - PLS regression KW - gaussian processes KW - soil KW - precision agriculture KW - nutrients Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-444183 SN - 1866-8372 IS - 815 ER - TY - JOUR A1 - Erler, Alexander A1 - Riebe, Daniel A1 - Beitz, Toralf A1 - Löhmannsröben, Hans-Gerd A1 - Gebbers, Robin T1 - Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR) JF - Sensors N2 - Precision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated. KW - LIBS KW - lasso KW - PLS regression KW - gaussian processes KW - soil KW - precision agriculture KW - nutrients Y1 - 2020 U6 - https://doi.org/10.3390/s20020418 SN - 1424-8220 VL - 20 IS - 2 PB - MDPI CY - Basel 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 - 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 - 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 -