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Updated: May 4, 2026

Automated Hydrophobic Interaction Chromatography Column Selection for Use in Protein Purification
Published on: September 21, 2011
Using the hydrophobic subtraction model to choose orthogonal columns for online comprehensive two-dimensional liquid
Rune Græsbøll1, Nikoline J Nielsen1, Jan H Christensen1
1University of Copenhagen, Faculty of Science, Department of Plant and Environmental Sciences, Thorvaldsensvej 40, DK-1871 Frederiksberg, Denmark.
Abstract:
A method for choosing orthogonal columns for a specific sample set in on-line comprehensive two-dimensional liquid chromatography (LC×LC) was developed on the basis of the hydrophobic subtraction model. The method takes into account the properties of the sample analytes by estimating new F-weights for the prediction of orthogonality. We compared sets of F-weights and used these F-weights to predict orthogonal column combinations: (1) the standard F-weights determined by Gilroy et al. [1], (2) F-weights determined from the retention of sample analytes, and the same procedure of calculation as described by Gilroy et al. [1], (3) F-weights determined from the retention of sample analytes but using principal component analysis (PCA) for the estimation, and (4) the Gilroy F-weights modified by excluding the C-term in the hydrophobic subtraction model, as suggested by Dolan and Snyder [2]. The retention of 13 neutral and 4 acidic oxygenated polycyclic aromatic compounds (PACs) and 3 nitrogen-containing PAC bases was measured isocratically on 12 columns. The isocratic runs were used to determine the hydrophobic subtraction model analyte parameters, and these were used to estimate new F-weights and predict orthogonal column combinations. LC×LC-DAD analysis was then performed on a test mix using these column sets. We found that the column combination predicted from the new F-weights provide a more orthogonal separation of the PACs than those predicted using the standard F-weights and the F-weights modified by excluding the C-term. This emphasizes the necessity of considering the nature of the sample when choosing orthogonal columns.
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