Selecting an "orthogonal" column during high-performance liquid chromatographic method development for samples that...

Area of Science:

• Analytical Chemistry
• Chromatography

Background:

• Reversed-phase selectivity is crucial for effective solute separation.
• Existing models for predicting column selectivity, like the hydrophobic-subtraction model, have limitations, particularly for non-ionized solutes.

Purpose of the Study:

• To adapt the hydrophobic-subtraction model for improved prediction of reversed-phase column selectivity for non-ionized solutes.
• To validate a modified model using a comprehensive column database and diverse solute samples.

Main Methods:

• Modification of the hydrophobic-subtraction model by removing the cation-exchange term, resulting in the F(s)(-C) parameter.
• Utilizing a database of 400 columns to identify maximally different columns for predicted separations.
• Validation using predicted separations for 64 non-ionized solutes.

Main Results:

• The modified model, F(s)(-C), provides improved predictions for reversed-phase selectivity of non-ionized solutes.
• The original F(s) parameter remains effective for ionized solutes.
• Combined use of F(s) and F(s)(-C) offers optimal predictions for mixed-solute samples.

Conclusions:

• The F(s)(-C) model is a valuable tool for selecting orthogonal columns for non-ionized solutes in reversed-phase chromatography.
• The hydrophobic-subtraction model can be effectively adapted for different solute types by adjusting its components.
• This approach aids in optimizing chromatographic method development and improving separation efficiency.