Optimizing gradient conditions in online comprehensive two‐dimensional reversed‐phase liquid chromatography by use of the linear solvent strength model

Abstract

The linear solvent strength model was used to predict coverage in online comprehensive two-dimensional reversed-phase liquid chromatography. The prediction model uses a parallelogram to describe the separation space covered with peaks in a system with limited orthogonality. The corners of the parallelogram are assumed to behave like chromatographic peaks and the position of these pseudo-compounds was predicted. A mix of 25 polycyclic aromatic compounds were used as a test. The precision of the prediction, span 0-25, was tested by varying input parameters, and was found to be acceptable with root mean square errors of 3. The accuracy of the prediction was assessed by comparing with the experimental coverages. Less than half of experimental coverages were outside prediction ± 1 × root mean square error and none outside prediction ± 2 × root mean square error. Accuracy was lower when retention factors were low, or when gradient conditions affected parameters not included in the model, e.g. second dimension gradient time affects the second dimension equilibration time. The concept shows promise as a tool for gradient optimization in online comprehensive two-dimensional liquid chromatography, as it mitigates the tedious registration and modeling of all sample constituents, a circumstance that is particularly appealing when dealing with complex samples.

Prediction of Analyte Retention Time in Liquid Chromatography

“…In a further application of the LSSM in retention modeling, Graesbøll and co-workers 75 have examined the prediction of coverage of the separation space in two-dimensional LC using RPLC as the separation mode for each dimension. Coverage (given by the parameter f cov ) is approximated by a parallelogram covering the elution positions of the sample analytes when plotted over the total possible two-dimensional elution space.…”

“…More recent developments of the LSSM have focused on the inclusion of further terms and comparison of linear and nonlinear models − and application to retention prediction for gradient elution. − These are discussed below.…”

“…In contrast, the mixed-mode model requires a numerical integration. The mixed-mode model may also fail to fit for MPs with a low concentration of water; however, Jandera et al have proposed the application of a three-parameter retention to accurately estimate the retention in pure organic solvent and pure water under both gradient and isocratic conditions.…”

Parallel gradients in comprehensive multidimensional liquid chromatography enhance utilization of the separation space and the degree of orthogonality when the separation mechanisms are correlated

“…The first LC ×LC system was implemented by Erni and Frei [1] who analyzed a complex plant extract. In the recent years, LC ×LC methods have been developed and applied among others to separate different kinds of polycyclic aromatic compounds [2] , pharmaceuticals [3] , proteins [4] , synthetic polymers [5] , natural products [6] and in proteomics [7] . Many applications have been reported in food analysis, including soybean [8] , corn oil [9] , wholegrain bread extracts [10] and plant extract [11] .…”