Chromatographic modeling as a tool in optimizing reversed-phase separation methods

ŠMAK, Pavel, Jana GREGOROVÁ, Lenka KUBINYIOVÁ, Jan ŠTINGL and Ondřej PEŠ. Chromatographic modeling as a tool in optimizing reversed-phase separation methods. In 23. Ročník školy hmotnostní spektrometrie. 2022. ISBN 978-80-88195-25-2.

Basic information

• Original name: Chromatographic modeling as a tool in optimizing reversed-phase separation methods
• Name in Czech: Modelování chromatografie jako nástroj při optimalizaci separace na reverzních fázích
• Authors: ŠMAK, Pavel, Jana GREGOROVÁ, Lenka KUBINYIOVÁ, Jan ŠTINGL and Ondřej PEŠ.
• Edition: 23. Ročník školy hmotnostní spektrometrie, 2022.

Other information

• Original language: English
• Type of outcome: Conference abstract
• Field of Study: 10609 Biochemical research methods
• Country of publisher: Czech Republic
• Confidentiality degree: is not subject to a state or trade secret
• WWW: Sborník
• Organization unit: Faculty of Medicine
• ISBN: 978-80-88195-25-2
• Keywords in English: chromatography; method optimization; reversed-phase chromatography

Abstract

Gradient elution can significantly enhance the separation in terms of the run time and peaks’ shape in HPLC. However, optimizing a gradient elution method can be a laborious process, especially if a larger number of compounds, significantly differing in the chromatographic behavior, needs to be separated. Multiple approaches are employed in order to obtain an ideal gradient composition over time – ranging from the “trial and error” methods to complex mathematical models. These models often rely on the Snyder’s equation and its modifications. In a typical setup, two or more separations are initially performed, and the results are fed to a software analysis tool, which tries to predict ideal conditions for the current system. Up to date available software tools for gradient run optimization lack either financial affordability or a feature-rich interface. A software tool developed in Python was developed, tested, and will be presented.

Links

• MUNI/A/1090/2021, interní kód MU: Name: Příspěvek {bio}chemických metod při studiu molekulární podstaty vybraných patologických jevů a onemocnění

Investor: Masaryk University