Publikation:
Error models for the kinetic evaluation of chemical degradation data

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dc.contributor.authorRanke, Johannes
dc.contributor.authorMeinecke, Stefan
dc.date.accessioned2024-06-16T14:29:41Z
dc.date.available2024-06-16T14:29:41Z
dc.date.issued2019
dc.description.abstractIn 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.comen
dc.format.extent1 Onlineressource (12 Seiten)
dc.identifier.doihttps://doi.org/10.60810/openumwelt-1387
dc.identifier.urihttps://openumwelt.de/handle/123456789/4767
dc.language.isoeng
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectChemische Zersetzung
dc.titleError models for the kinetic evaluation of chemical degradation data
dc.title.alternativeError models for the kinetic evaluation of chemical degradation data
dc.typeWissenschaftlicher Artikel
dspace.entity.typePublication
local.bibliographicCitation.journalTitleEnvironments
local.bibliographicCitation.originalDOI10.3390/environments6120124
local.bibliographicCitation.volume6 (2019), Heft 12
local.collectionAufsätze
local.contributor.authorId(DE-588)123957974
local.contributor.authorId02062906
local.identifier.catalogId02471791
local.ingest.leader05068naa a2200000uu 4500
local.jointTitleERROR MODELS FOR THE KINETIC EVALUATION OF CHEMICAL DEGRADATION DATA
local.reviewtrue
local.sourcecatalog
local.staffPublicationtrue
relation.isAuthorOfPublication53ebc1dc-9427-40ce-b468-be246b1402c8
relation.isAuthorOfPublication.latestForDiscovery53ebc1dc-9427-40ce-b468-be246b1402c8
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