Auflistung nach Autor:in "Ranke, Johannes"

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A new approach to simplify degradation kinetics
(2022) Ranke, Johannes; Wöltjen, Janina
Application of nonlinear hierarchical models to the kinetic evaluation of chemical degradation data
(Umweltbundesamt, 2023) Ranke, Johannes; Deutschland. Umweltbundesamt; Wöltjen, Janina; Meinecke, Stefan
Derzeit werden chemische Abbaudaten ausgewertet, indem verschiedene nichtlineare Regressionsmodelle einzeln auf die verfügbaren Datensätze angewandt werden. In vielen Fällen können dabei einige der Abbauparameter nicht für alle Datensätze verlässlich bestimmt werden. Die aktuell gültigen regulatorischen Leitlinien empfehlen in solchen Fällen die Verwendung von mehr oder weniger willkürlich gewählten Standardwerten für diese Parameter. Des Weiteren ergeben oft unterschiedliche Modelle die beste Anpassung in den verschiedenen Datensätzen, so dass mittlere Modellparameter mit Hilfe von Behelfslösungen mit schwacher wissenschaftlicher Grundlage bestimmt werden müssen. Beide Probleme können vermieden werden, wenn hierarchische nichtlineare Modelle verwendet werden, bei denen Parameterverteilungen an die Gesamtheit der Daten angepasst werden. In diesem Bericht wird eine kurze Einführung in diesen Modelltyp gegeben. Weiterhin wird die Verwendung einer R markdown Vorlage und einer Tabellenkalkulationsdatei für die Eingabe von Daten beschrieben. Beide Dateien wurden kürzlich in das R-Paket mkin integriert und erleichtern damit die Anwendung dieser Methode auf neue Daten. Um hierarchische kinetische Modelle in der regulatorischen Auswertung von Abbaudaten zu etablieren, müsste ein Leitfaden erarbeitet werden, in dem erläutert wird, wie die Ergebnisse der hierarchischen Abbaukinetiken in den verschiedenen regulatorischen Anwendungsbereichen verwendet werden sollten. Quelle: Forschungsbericht
Comparison of software tools for kinetic evaluation of chemical degradation data
(2018) Ranke, Johannes; Meinecke, Stefan; Wöltjen, Janina
Background 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. Results Test datasets were assembled that can be used to compare the numerical results for different software tools. These datasets can also be used to ensure that no unintended or erroneous behaviour is introduced in newer versions. In the comparison of numerical results, good agreement between the parameter estimates was observed for datasets with up to three metabolites. For the now unmaintained reference software DegKinManager/ModelMaker, and for OpenModel which is still under development, user options were identified that should be taken care of in order to obtain results that are as reliable as possible. Based on the scoring system mentioned above, the software tools gmkin, KinGUII and CAKE received the best scores for use type I. Out of the 15 software packages compared with respect to use type II, again gmkin and KinGUII were the first two, followed by the script based tool mkin, which is the technical basis for gmkin, and by OpenModel. Conclusions Based on the evaluation using the system of criteria mentioned above and the comparison of numerical results for the suite of test datasets, the software tools gmkin, KinGUII and CAKE are recommended for use type I, and gmkin and KinGUII for use type II. For users that prefer to work with scripts instead of graphical user interfaces, mkin is recommended. For future software evaluations, it is recommended to include a measure for the total time that a typical user needs for a kinetic evaluation into the scoring scheme. It is the hope of the authors that the publication of test data, source code and overall rankings foster the evolution of useful and reliable software in the field. © The Author(s) 2018
Degradation kinetics on the next level
(2022) Ranke, Johannes; Wöltjen, Janina
Error models for the kinetic evaluation of chemical degradation data
(2019) Ranke, Johannes; Meinecke, Stefan
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. Quelle: http://www.mdpi.com
Prüfung und Validierung von Software zur kinetischen Auswertung von Abbaudaten
(2014) Meinecke, Stefan; Ranke, Johannes; Winkler, Marita