Separation quality factor as a tool for method screening and column comparison in liquid chromatography.

Chromatographic method development and optimization remain complex tasks due to the multivariate nature of separation quality. While retention modeling tools and design of experiments (DoE) approaches enable efficient exploration of the chromatographic space, they typically rely on single performance criteria or require a relatively large number of experimental runs. In this work we demonstrate, for the first time, the application of a normalized multi-parameter separation quality factor (SQF) as a predictive response function for method screening and column comparison. A simplified retention modeling strategy based on only three calibration experiments was applied to predict retention time, peak width, and peak symmetry as a function of gradient time (steepness) and mobile phase temperature. These predicted peak properties were then used to compute SQF and its individual sub-metrics across the design space, allowing the generation of two-dimensional SQF response surfaces. The approach was demonstrated using pharmaceutical impurity mixtures, as a case studies. Experimental validation confirmed good agreement between predicted and measured chromatograms, with predicted SQF values closely matching experimental results. Furthermore, a novel strategy for column comparison based on SQF difference mapping is introduced. By subtracting SQF surfaces obtained for different stationary phases, it becomes possible to directly visualize regions where one column outperforms another, providing a powerful and intuitive tool for column selection.