@article{MuellerHelmsRohreretal.2020,
  author    = {M{\"u}ller, Anke Katharina and Helms, Ute and Rohrer, Carsten and M{\"o}hler, Monika and Hellwig, Frank and Glei, Michael and Schwerdtle, Tanja and Lorkowski, Stefan and Dawczynski, Christine},
  title     = {Nutrient composition of different hazelnut cultivars grown in Germany},
  series = {Foods},
  volume    = {9},
  journal   = {Foods},
  number    = {11},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2304-8158},
  doi       = {10.3390/foods9111596},
  pages     = {11},
  year      = {2020},
  abstract  = {Hazelnuts are rarely cultivated in Germany, although they are a valuable source for macro- and micronutrients and can thus contribute to a healthy diet. Near the present, 15 varieties were cultivated in Thuringia, Germany, as a pilot study for further research. The aim of our study was to evaluate the micro- and macronutrient composition of representative, randomly mixed samples of the 15 different hazelnut cultivars. Protein, fat, and fiber contents were determined using established methods. Fatty acids, tocopherols, minerals, trace elements, and ultra-trace elements were analyzed using gas chromatography, high-performance liquid chromatography, and inductively coupled plasma triple quadrupole mass-spectrometry, respectively. We found that the different hazelnut varieties contained valuable amounts of fat, protein, dietary fiber, minerals, trace elements, and alpha-tocopherol, however, in different quantities. The variations in nutrient composition were independent of growth conditions, which were identical for all hazelnut varieties. Therefore, each hazelnut cultivar has its specific nutrient profile.},
  language  = {en}
}
@article{KaercherFilstrupBraunsetal.2020,
  author    = {K{\"a}rcher, Oskar and Filstrup, Christopher T. and Brauns, Mario and Tasevska, Orhideja and Patceva, Suzana and Hellwig, Niels and Walz, Ariane and Frank, Karin and Markovic, Danijela},
  title     = {Chlorophyll a relationships with nutrients and temperature, and predictions for lakes across perialpine and Balkan mountain regions},
  series = {Inland Waters},
  volume    = {10},
  journal   = {Inland Waters},
  number    = {1},
  publisher = {Taylor \& Francis},
  address   = {London},
  issn      = {2044-2041},
  doi       = {10.1080/20442041.2019.1689768},
  pages     = {29 -- 41},
  year      = {2020},
  abstract  = {Model-derived relationships between chlorophyll a (Chl-a) and nutrients and temperature have fundamental implications for understanding complex interactions among water quality measures used for lake classification, yet accuracy comparisons of different approaches are scarce. Here, we (1) compared Chl-a model performances across linear and nonlinear statistical approaches; (2) evaluated single and combined effects of nutrients, depth, and temperature as lake surface water temperature (LSWT) or altitude on Chl-a; and (3) investigated the reliability of the best water quality model across 13 lakes from perialpine and central Balkan mountain regions. Chl-a was modelled using in situ water quality data from 157 European lakes; elevation data and LSWT in situ data were complemented by remote sensing measurements. Nonlinear approaches performed better, implying complex relationships between Chl-a and the explanatory variables. Boosted regression trees, as the best performing approach, accommodated interactions among predictor variables. Chl-a-nutrient relationships were characterized by sigmoidal curves, with total phosphorus having the largest explanatory power for our study region. In comparison with LSWT, utilization of altitude, the often-used temperature surrogate, led to different influence directions but similar predictive performances. These results support utilizing altitude in models for Chl-a predictions. Compared to Chl-a observations, Chl-a predictions of the best performing approach for mountain lakes (oligotrophic-eutrophic) led to minor differences in trophic state categorizations. Our findings suggest that both models with LSWT and altitude are appropriate for water quality predictions of lakes in mountain regions and emphasize the importance of incorporating interactions among variables when facing lake management challenges.},
  language  = {en}
}
@misc{KaercherFilstrupBraunsetal.2020,
  author    = {K{\"a}rcher, Oskar and Filstrup, Christopher T. and Brauns, Mario and Tasevska, Orhideja and Patceva, Suzana and Hellwig, Niels and Walz, Ariane and Frank, Karin and Markovic, Danijela},
  title     = {Chlorophyll a relationships with nutrients and temperature, and predictions for lakes across perialpine and Balkan mountain regions},
  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    = {1},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-51527},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-515271},
  pages     = {15},
  year      = {2020},
  abstract  = {Model-derived relationships between chlorophyll a (Chl-a) and nutrients and temperature have fundamental implications for understanding complex interactions among water quality measures used for lake classification, yet accuracy comparisons of different approaches are scarce. Here, we (1) compared Chl-a model performances across linear and nonlinear statistical approaches; (2) evaluated single and combined effects of nutrients, depth, and temperature as lake surface water temperature (LSWT) or altitude on Chl-a; and (3) investigated the reliability of the best water quality model across 13 lakes from perialpine and central Balkan mountain regions. Chl-a was modelled using in situ water quality data from 157 European lakes; elevation data and LSWT in situ data were complemented by remote sensing measurements. Nonlinear approaches performed better, implying complex relationships between Chl-a and the explanatory variables. Boosted regression trees, as the best performing approach, accommodated interactions among predictor variables. Chl-a-nutrient relationships were characterized by sigmoidal curves, with total phosphorus having the largest explanatory power for our study region. In comparison with LSWT, utilization of altitude, the often-used temperature surrogate, led to different influence directions but similar predictive performances. These results support utilizing altitude in models for Chl-a predictions. Compared to Chl-a observations, Chl-a predictions of the best performing approach for mountain lakes (oligotrophic-eutrophic) led to minor differences in trophic state categorizations. Our findings suggest that both models with LSWT and altitude are appropriate for water quality predictions of lakes in mountain regions and emphasize the importance of incorporating interactions among variables when facing lake management challenges.},
  language  = {en}
}