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% This file was originally created with JabRef 2.7b.
% Encoding: ISO8859_1
@BOOK{bates1988,
title = {Nonlinear regression and its applications},
publisher = {Wiley-Interscience},
year = {1988},
author = {D. Bates and D. Watts}
}
@MANUAL{FOCUSkinetics2011,
title = {Generic guidance for estimating persistence and degradation kinetics
from environmental fate studies on pesticides in EU registration},
author = {{FOCUS Work Group on Degradation Kinetics}},
edition = {1.0},
month = {November},
year = {2011},
file = {FOCUS kinetics 2011 Generic guidance:/home/ranke/dok/orgs/focus/FOCUSkineticsvc_1_0_Nov23.pdf:PDF},
url = {http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics}
}
@MANUAL{FOCUSkinetics2014,
title = {Generic guidance for estimating persistence and degradation kinetics
from environmental fate studies on pesticides in EU registration},
author = {{FOCUS Work Group on Degradation Kinetics}},
edition = {1.1},
month = {December},
year = {2014},
file = {FOCUS kinetics 2011 Generic guidance:/home/ranke/dok/orgs/focus/dk/FOCUSkineticsvc1.1Dec2014.pdf:PDF},
url = {http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics}
}
@MANUAL{FOCUS2006,
title = {Guidance Document on Estimating Persistence and Degradation Kinetics
from Environmental Fate Studies on Pesticides in EU Registration.
Report of the FOCUS Work Group on Degradation Kinetics},
author = {{FOCUS Work Group on Degradation Kinetics}},
year = {2006},
note = {EC Document Reference Sanco/10058/2005 version 2.0},
url = {http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics}
}
@MANUAL{rcore2016,
title = {\textsf{R}: A Language and Environment for Statistical Computing},
author = {{R Development Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2016},
note = {{ISBN} 3-900051-07-0},
url = {https://www.R-project.org}
}
@MANUAL{pkg:mkin,
title = {`{mkin}`: {K}inetic evaluation of chemical degradation data},
author = {J. Ranke},
year = {2021},
url = {https://CRAN.R-project.org/package=mkin}
}
@Inproceedings{ schaefer2007,
title = {{KinGUI}: a new kinetic software tool for evaluations according to {FOCUS} degradation kinetics},
author = {D. Sch\"{a}fer and B. Mikolasch and P. Rainbird and B. Harvey},
booktitle = {Proceedings of the XIII Symposium Pesticide Chemistry},
editor = {Del Re A. A. M. and Capri E. and Fragoulis G. and Trevisan M.},
year = {2007},
address = {Piacenza},
pages = {916--923}
}
@ARTICLE{soetaert2010,
author = {Karline Soetaert and Thomas Petzoldt},
title = {Inverse Modelling, Sensitivity and Monte Carlo Analysis in {R} Using
Package {FME}},
journal = {Journal of Statistical Software},
year = {2010},
volume = {33},
pages = {1--28},
number = {3},
url = {https://www.jstatsoft.org/v33/i03/}
}
@Inproceedings{ ranke2012,
title = {Parameter reliability in kinetic evaluation of environmental metabolism data - Assessment and the influence of model specification},
author = {J. Ranke and R. Lehmann},
booktitle = {SETAC World 20-24 May},
year = {2012},
address = {Berlin}
}
@Inproceedings{ ranke2015,
title = {To t-test or not to t-test, that is the question},
author = {J. Ranke and R. Lehmann},
booktitle = {XV Symposium on Pesticide Chemistry 2-4 September 2015},
year = {2015},
address = {Piacenza},
url = {http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf}
}
@Techreport{ranke2014,
title = {{Prüfung und Validierung von Modellierungssoftware als Alternative zu
ModelMaker 4.0}},
author = {J. Ranke},
year = 2014,
institution = {Umweltbundesamt},
volume = {Projektnummer 27452}
}
@Article{ranke2018,
author="Ranke, Johannes
and W{\"o}ltjen, Janina
and Meinecke, Stefan",
title="Comparison of software tools for kinetic evaluation of chemical degradation data",
journal="Environmental Sciences Europe",
year="2018",
month="May",
day="18",
volume="30",
number="1",
pages="17",
abstract="For evaluating the fate of xenobiotics in the environment, a variety of degradation or environmental metabolism experiments are routinely conducted. The data generated in such experiments are evaluated by optimizing the parameters of kinetic models in a way that the model simulation fits the data. No comparison of the main software tools currently in use has been published to date. This article shows a comparison of numerical results as well as an overall, somewhat subjective comparison based on a scoring system using a set of criteria. The scoring was separately performed for two types of uses. Uses of type I are routine evaluations involving standard kinetic models and up to three metabolites in a single compartment. Evaluations involving non-standard model components, more than three metabolites or more than a single compartment belong to use type II. For use type I, usability is most important, while the flexibility of the model definition is most important for use type II.",
issn="2190-4715",
doi="10.1186/s12302-018-0145-1",
url="https://doi.org/10.1186/s12302-018-0145-1"
}
@Article{ranke2019,
author = {Ranke, Johannes and Meinecke, Stefan},
title = {Error Models for the Kinetic Evaluation of Chemical Degradation Data},
journal = {Environments},
year = {2019},
volume = {6},
number = {12},
issn = {2076-3298},
abstract = {In the kinetic evaluation of chemical degradation data, degradation models are fitted to the data by varying degradation model parameters to obtain the best possible fit. Today, constant variance of the deviations of the observed data from the model is frequently assumed (error model “constant variance”). Allowing for a different variance for each observed variable (“variance by variable”) has been shown to be a useful refinement. On the other hand, experience gained in analytical chemistry shows that the absolute magnitude of the analytical error often increases with the magnitude of the observed value, which can be explained by an error component which is proportional to the true value. Therefore, kinetic evaluations of chemical degradation data using a two-component error model with a constant component (absolute error) and a component increasing with the observed values (relative error) are newly proposed here as a third possibility. In order to check which of the three error models is most adequate, they have been used in the evaluation of datasets obtained from pesticide evaluation dossiers published by the European Food Safety Authority (EFSA). For quantitative comparisons of the fits, the Akaike information criterion (AIC) was used, as the commonly used error level defined by the FOrum for the Coordination of pesticide fate models and their USe(FOCUS) is based on the assumption of constant variance. A set of fitting routines was developed within the mkin software package that allow for robust fitting of all three error models. Comparisons using parent only degradation datasets, as well as datasets with the formation and decline of transformation products showed that in many cases, the two-component error model proposed here provides the most adequate description of the error structure. While it was confirmed that the variance by variable error model often provides an improved representation of the error structure in kinetic fits with metabolites, it could be shown that in many cases, the two-component error model leads to a further improvement. In addition, it can be applied to parent only fits, potentially improving the accuracy of the fit towards the end of the decline curve, where concentration levels are lower.},
article-number = {124},
doi = {10.3390/environments6120124},
url = {https://www.mdpi.com/2076-3298/6/12/124},
}
@Article{gao11,
Title = {Improving uncertainty analysis in kinetic evaluations using iteratively reweighted least squares},
Author = {Gao, Z. and Green, J.W. and Vanderborght, J. and Schmitt, W.},
Journal = {Environmental Science and Technology},
Year = {2011},
Pages = {4429-4437},
Volume = {45},
Type = {Journal}
}
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