Retention prediction and separation optimization under multilinear gradient elution in liquid chromatography with...

Area of Science:

• Analytical Chemistry
• Chromatography

Background:

• Gradient elution chromatography is vital for separating complex mixtures.
• Accurate modeling of retention data is crucial for optimizing separation.
• Existing methods for modeling gradient elution can be complex and time-consuming.

Purpose of the Study:

• To develop and evaluate Excel VBA macros for modeling multilinear gradient retention data.
• To implement and compare four distinct methods for applying chromatographic models.
• To create fitting and optimization platforms for enhanced chromatographic analysis.

Main Methods:

• Development of Excel VBA macros for ten chromatographic models.
• Application of four methods: analytical expression, Nikitas-Pappa approach, stepwise approximation, and numerical integration (trapezoid rule).
• Implementation on fitting and optimization platforms using experimental and artificial data.

Main Results:

• The developed VBA macros effectively perform fitting, prediction, and optimization of chromatographic data.
• Both fitting and optimization platforms demonstrated ease of use and effectiveness across various conditions.
• The analytical and Nikitas-Pappa methods showed the best performance for modeling gradient elution data.

Conclusions:

• Excel VBA macros provide a powerful and accessible tool for modeling gradient elution chromatography.
• The developed fitting and optimization platforms streamline the analysis and optimization of chromatographic separations.
• The analytical and Nikitas-Pappa approaches are recommended for their superior performance in modeling retention data.