Optimization of selectivity in high-performance liquid chromatography using desirability functions and mixture...

Area of Science:

• Analytical Chemistry
• Chromatography
• Chemometrics

Background:

• High-performance liquid chromatography (HPLC) is a crucial technique for chemical separation.
• Optimizing mobile phase composition is vital for achieving high-quality HPLC separations.
• Statistical methods can enhance the efficiency and accuracy of HPLC method development.

Purpose of the Study:

• To develop and evaluate a computer program for mobile phase optimization in HPLC.
• To utilize the desirability function technique combined with prisma mixture design for enhanced separation quality.
• To predict retention times and band broadening using statistical models for dansyl amides and coumarins.

Main Methods:

• Implementation of a computer program integrating desirability function and prisma mixture design.
• Application of quadratic regression models to predict retention times (tR).
• Utilization of linear regression models to describe band broadening (wh).
• Calculation and prediction of resolution (Rs) using developed models.
• Determination of overall optimum using overall desirability (D).

Main Results:

• Quadratic regression models accurately predicted tR values for dansyl amides and coumarins based on eluent composition.
• Linear regression models effectively described band broadening.
• The developed models allowed accurate prediction of resolution (Rs) across various solvent combinations.
• Optimal eluent mixtures were identified using contour plots, leading to good separation.

Conclusions:

• The computer program successfully optimizes mobile phase composition for HPLC separations.
• The combination of desirability function and prisma design provides an effective approach for method development.
• The program offers flexibility for optimizing critical pairs or achieving overall separation goals.