Retention Modeling in an Extended Knowledge Space

The goal of this manuscript was to examine the expansibility of the prediction ranges of the software, DryLab with small molecules. The final part of method development is method optimization in which we aim to cover a relatively narrow range where a promising region can be found. Understanding peak...

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Bibliographic Details
Published in:Chromatographia 2018-04, Vol.81 (4), p.585-594
Main Authors: Rácz, Norbert, Kormány, Róbert
Format: Article
Language:English
Subjects:
Analytical Chemistry
Chemistry
Chemistry and Materials Science
Chromatography
Laboratory Medicine
Original
Pharmacy
Proteomics
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Summary:The goal of this manuscript was to examine the expansibility of the prediction ranges of the software, DryLab with small molecules. The final part of method development is method optimization in which we aim to cover a relatively narrow range where a promising region can be found. Understanding peak movements, DryLab proved to be a powerful tool but the prediction range is limited by the recommendations of the manufacturer. We aimed to examine these limits in a huge knowledge space for enhancing the speed of method fine tuning.
ISSN:0009-5893
1612-1112
DOI:10.1007/s10337-017-3466-0