@misc{11495,
  abstract     = {{To evaluate the suitability of an analytical instrument, essential figures of merit such as the limit of detection (LOD) and the limit of quantification (LOQ) can be employed. However, as the definitions k nown in the literature are mostly applicable to one signal per sample, estimating the LOD for substances with instruments yielding multidimensional results like electronic noses (eNoses) is still challenging. In this paper, we will compare and present different approaches to estimate the LOD for eNoses by employing commonly used multivariate data analysis and regression techniques, including principal component analysis (PCA), principal component regression (PCR), as well as partial least squares regression (PLSR). These methods could subsequently be used to assess the suitability of eNoses to help control and steer processes where volatiles are key process parameters. As a use case, we determined the LODs for key compounds involved in beer maturation, namely acetaldehyde, diacetyl, dimethyl sulfide, ethyl acetate, isobutanol, and 2-phenylethanol, and discussed the suitability of our eNose for that dertermination process. The results of the methods performed demonstrated differences of up to a factor of eight. For diacetyl, the LOD and the LOQ were sufficiently low to suggest potential for monitoring via eNose. }},
  author       = {{Kruse, Julia and Wörner, Julius and Schneider, Jan and Dörksen, Helene and Pein-Hackelbusch, Miriam}},
  booktitle    = {{Sensors}},
  issn         = {{1424-8220 }},
  keywords     = {{multidimensional sensor arrays, MOS sensors, beer fermentation, process control, gas analysis, metal oxide semiconductors, intentional data analysis, chemometrics, PLSR, PCA, first-order calibration}},
  number       = {{11}},
  publisher    = {{MDPI}},
  title        = {{{Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses }}},
  doi          = {{10.3390/s24113520}},
  volume       = {{24}},
  year         = {{2024}},
}

@misc{12230,
  abstract     = {{Model ensembles have several benefits compared to single-model applications but are not frequently used within the lake modelling community. Setting up and running multiple lake models can be challenging and time consuming, despite the many similarities between the existing models (forcing data, hypsograph, etc.). Here we present an R package, LakeEnsemblR, that facilitates running ensembles of five different vertical one-dimensional hydrodynamic lake models (FLake, GLM, GOTM, Simstrat, MyLake). The package requires input in a standardised format and a single configuration file. LakeEnsemblR formats these files to the input required by each model, and provides functions to run and calibrate the models. The outputs of the different models are compiled into a single file, and several post-processing operations are supported. LakeEnsemblR's workflow standardisation can simplify model benchmarking and uncertainty quantification, and improve collaborations between scientists. We showcase the successful application of LakeEnsemblR for two different lakes.}},
  author       = {{Moore, Tadhg N. and Mesman, Jorrit P. and Ladwig, Robert and Feldbauer, Johannes and Olsson, Freya and Pilla, Rachel M. and Shatwell, Tom and Venkiteswaran, Jason J. and Delany, Austin D. and Dugan, Hilary and Rose, Kevin C. and Read, Jordan S.}},
  booktitle    = {{Environmental modelling & software with environment data news}},
  issn         = {{1873-6726}},
  keywords     = {{Ensemble modeling, Vertical one-dimensional lake model, R package, Calibration, Thermal structure, Hydrodynamics}},
  publisher    = {{Elsevier BV}},
  title        = {{{LakeEnsemblR: An R package that facilitates ensemble modelling of lakes}}},
  doi          = {{10.1016/j.envsoft.2021.105101}},
  volume       = {{143}},
  year         = {{2021}},
}