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

• Published in: Journal of separation science Vol. 40; no. 18; pp. 3612 - 3620
• Main Authors: Græsbøll, Rune, Janssen, Hans‐Gerd, Christensen, Jan H., Nielsen, Nikoline J.
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01.09.2017
• Subjects: Aromatic compounds; Chromatography; linear solvent strength model; Mathematical models; Mean square errors; Mean square values; Optimization; Orthogonality; polycyclic aromatic compounds; Prediction models; Root-mean-square errors; Solvents; surface coverage; Two dimensional models; two‐dimensional liquid chromatography; linear solvent strength model; orthogonality; surface coverage; polycyclic aromatic compounds; two-dimensional liquid chromatography
• ISSN: 1615-9306; 1615-9314
• DOI: 10.1002/jssc.201700239

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.