2019
DOI: 10.1002/kin.21316
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QUANTIS: Data quality assessment tool by clustering analysis

Abstract: Automatically generated kinetic networks are ideally validated against a large set of accurate, reproducible, and easy‐to‐model experimental data. However, although this might seem simple, it proves to be quite challenging. QUANTIS, a publicly available Python package, is specifically developed to evaluate both the precision and accuracy of experimental data and to ensure a uniform, quick processing, and storage strategy that enables automated comparison of developed kinetic models. The precision is investigat… Show more

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Cited by 9 publications
(6 citation statements)
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References 58 publications
(84 reference statements)
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“…Principal component analysis (PCA) is performed on this data using an in-house developed data analysis tool, Quantis. 31 After PCA, outliers for each of the conditions are removed and the remaining product yields are averaged for each of the conditions. The outliers identified by PCA are generally the first injection after a new temperature profile is set and thus it can be assumed that these outliers occur because steady state operation has not been fully reached.…”
Section: Methodsmentioning
confidence: 99%
“…Principal component analysis (PCA) is performed on this data using an in-house developed data analysis tool, Quantis. 31 After PCA, outliers for each of the conditions are removed and the remaining product yields are averaged for each of the conditions. The outliers identified by PCA are generally the first injection after a new temperature profile is set and thus it can be assumed that these outliers occur because steady state operation has not been fully reached.…”
Section: Methodsmentioning
confidence: 99%
“…Based on the FID response for the internal standard, the yields of all other products were calculated by using the molar response factor (MRF) approach [73]. All reported yields were normalized via the experimental postprocessing tool QUANTIS [74].…”
Section: Product Analysismentioning
confidence: 99%
“…This makes the post-processing of the experiments less trivial as the effect of the factors is not isolated. As a result, a statistical analysis is required to draw conclusions from the experimental campaign [52]. These tools are incorporated in regular DoE software but not in the active machine learning packages that are available as of today.…”
Section: Ease-of-usementioning
confidence: 99%