Models and objective functions for the optimisation of selectivity in reversed-phase liquid chromatography

Area of Science:

• Analytical Chemistry
• Separation Science

Background:

• Chromatographic method development relies on optimizing experimental factors.
• Predictive models are crucial for efficient optimization and understanding system behavior.

Purpose of the Study:

• To present interpretive methodologies for optimizing chromatographic conditions.
• To detail models and procedures for predicting chromatographic performance.

Main Methods:

• Utilizing models and algorithms to infer system behavior under varying experimental factors.
• Designing experiments to gather data for model building.
• Incorporating factors like organic modifiers, pH, and temperature into retention models.

Main Results:

• Models can predict chromatographic performance for new conditions.
• Retention models significantly impact the prediction of chromatographic resolution.
• Consideration of skewed peaks and their effect on peak profiles is included.

Conclusions:

• Interpretive methodologies offer efficient tools for optimal chromatography.
• Accurate retention prediction is key to enhancing chromatographic resolution.
• The study discusses assessment of resolution and practical suitability of optimal conditions.