@article{RuehlmannBuecheleOstermannetal.2018,
  author    = {R{\"u}hlmann, Madlen and B{\"u}chele, Dominique and Ostermann, Markus and Bald, Ilko and Schmid, Thomas},
  title     = {Challenges in the quantification of nutrients in soils using laser-induced breakdown spectroscopy},
  series = {Spectrochimica Acta Part B: Atomic Spectroscopy},
  volume    = {146},
  journal   = {Spectrochimica Acta Part B: Atomic Spectroscopy},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0584-8547},
  doi       = {10.1016/j.sab.2018.05.003},
  pages     = {115 -- 121},
  year      = {2018},
  abstract  = {The quantification of the elemental content in soils with laser-induced breakdown spectroscopy (LIBS) is challenging because of matrix effects strongly influencing the plasma formation and LIBS signal. Furthermore, soil heterogeneity at the micrometre scale can affect the accuracy of analytical results. In this paper, the impact of univariate and multivariate data evaluation approaches on the quantification of nutrients in soil is discussed. Exemplarily, results for calcium are shown, which reflect trends also observed for other elements like magnesium, silicon and iron. For the calibration models, 16 certified reference soils were used. With univariate and multivariate approaches, the calcium mass fractions in 60 soils from different testing grounds in Germany were calculated. The latter approach consisted of a principal component analysis (PCA) of adequately pre-treated data for classification and identification of outliers, followed by partial least squares regression (PLSR) for quantification. For validation, the soils were also characterised with inductively coupled plasma optical emission spectroscopy (ICP OES) and X-ray fluorescence (XRF) analysis. Deviations between the LIBS quantification results and the reference analytical results are discussed.},
  language  = {en}
}
@misc{BuecheleChaoOstermannetal.2019,
  author    = {B{\"u}chele, Dominique and Chao, Madlen and Ostermann, Markus and Leenen, Matthias and Bald, Ilko},
  title     = {Multivariate chemometrics as a key tool for prediction of K and Fe in a diverse German agricultural soil-set using EDXRF},
  series = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam Mathematisch-Naturwissenschaftliche Reihe},
  number    = {784},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-43998},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-439988},
  pages     = {11},
  year      = {2019},
  abstract  = {Within the framework of precision agriculture, the determination of various soil properties is moving into focus, especially the demand for sensors suitable for in-situ measurements. Energy-dispersive X-ray fluorescence (EDXRF) can be a powerful tool for this purpose. In this study a huge diverse soil set (n = 598) from 12 different study sites in Germany was analysed with EDXRF. First, a principal component analysis (PCA) was performed to identify possible similarities among the sample set. Clustering was observed within the four texture classes clay, loam, silt and sand, as clay samples contain high and sandy soils low iron mass fractions. Furthermore, the potential of uni- and multivariate data evaluation with partial least squares regression (PLSR) was assessed for accurate determination of nutrients in German agricultural samples using two calibration sample sets. Potassium and iron were chosen for testing the performance of both models. Prediction of these nutrients in 598 German soil samples with EDXRF was more accurate using PLSR which is confirmed by a better overall averaged deviation and PLSR should therefore be preferred.},
  language  = {en}
}
@article{BuecheleChaoOstermannetal.2019,
  author    = {B{\"u}chele, Dominique and Chao, Madlen and Ostermann, Markus and Leenen, Matthias and Bald, Ilko},
  title     = {Multivariate chemometrics as a key tool for prediction of K and Fe in a diverse German agricultural soil-set using EDXRF},
  series = {Scientific Reports},
  volume    = {9},
  journal   = {Scientific Reports},
  publisher = {Macmillan Publishers Limited, part of Springer Nature},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/s41598-019-53426-5},
  pages     = {11},
  year      = {2019},
  abstract  = {Within the framework of precision agriculture, the determination of various soil properties is moving into focus, especially the demand for sensors suitable for in-situ measurements. Energy-dispersive X-ray fluorescence (EDXRF) can be a powerful tool for this purpose. In this study a huge diverse soil set (n = 598) from 12 different study sites in Germany was analysed with EDXRF. First, a principal component analysis (PCA) was performed to identify possible similarities among the sample set. Clustering was observed within the four texture classes clay, loam, silt and sand, as clay samples contain high and sandy soils low iron mass fractions. Furthermore, the potential of uni- and multivariate data evaluation with partial least squares regression (PLSR) was assessed for accurate determination of nutrients in German agricultural samples using two calibration sample sets. Potassium and iron were chosen for testing the performance of both models. Prediction of these nutrients in 598 German soil samples with EDXRF was more accurate using PLSR which is confirmed by a better overall averaged deviation and PLSR should therefore be preferred.},
  language  = {en}
}