@article{CzolkosDockTonningetal.2016,
  author    = {Czolkos, Ilja and Dock, Eva and Tonning, Erik and Christensen, Jakob and Winther-Nielsen, Margrethe and Carlsson, Charlotte and Mojzikova, Renata and Skladal, Petr and Wollenberger, Ursula and Norgaard, Lars and Ruzgas, Tautgirdas and Emneus, Jenny},
  title     = {Prediction of wastewater quality using amperometric bioelectronic tongues},
  series = {Marine policy},
  volume    = {75},
  journal   = {Marine policy},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0956-5663},
  doi       = {10.1016/j.bios.2015.08.055},
  pages     = {375 -- 382},
  year      = {2016},
  abstract  = {Wastewater samples from a Swedish chemi-thermo-mechanical pulp (CTMP) mill collected at different purification stages in a wastewater treatment plant (WWTP) were analyzed with an amperometric enzyme-based biosensor array in a flow-injection system. In order to resolve the complex composition of the wastewater, the array consists of several sensing elements which yield a multidimensional response. We used principal component analysis (PCA) to decompose the array's responses, and found that wastewater with different degrees of pollution can be differentiated. With the help of partial least squares regression (PLS-R), we could link the sensor responses to the toxicity parameter, as well as to global organic pollution parameters (COD, BOD, and TOC). From investigating the influences of individual sensors in the array, it was found that the best models were in most cases obtained when all sensors in the array were included in the PLS-R model. We find that fast simultaneous determination of several global environmental parameters characterizing wastewaters is possible with this kind of biosensor array, in particular because of the link between the sensor responses and the biological effect onto the ecosystem into which the wastewater would be released. In conjunction with multivariate data analysis tools, there is strong potential to reduce the total time until a result is yielded from days to a few minutes.},
  language  = {en}
}
@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}
}