Accuracy of retention model parameters obtained from retention data in liquid chromatography. Issue 17 (4th April 2022)

• Record Type: Journal Article
• Authors: Brau, Tyler; Pirok, Bob; Rutan, Sarah; Stoll, Dwight
• Is Part Of: Journal of separation science. Volume 45:Issue 17(2022)
• Page Start: 3241
• Page End: 3255
• Publication Date: 2022-04-04
• Subjects: gradient retention factor -- linear solvent strength theory -- Neue‐Kuss -- retention model; Separation (Technology) -- Periodicals; Chromatographic analysis -- Periodicals; 543.089
• DOI: 10.1002/jssc.202100911
• Languages: English
• ISSNs: 1615-9306

Abstract

Abstract : In liquid chromatography, it is often very useful to have an accurate model of the retention factor, k, over a wide range of isocratic elution conditions. In principle, the parameters of a retention model can be obtained by fitting either isocratic or gradient retention factor data. However, in spite of many of our own attempts to accurately predict isocratic k values using retention models trained with gradient retention data, this has not worked in our hands. In the present study, we have used synthetic isocratic and gradient retention data for small molecules under reversed‐phase liquid chromatography conditions. This allows us to discover challenges associated with predicting isocratic k values without the confounding influences of experimental issues that are difficult to model or eliminate. The results indicate that it is not currently possible to consistently predict isocratic retention factors for small molecules with accuracies better than 10%, even when using synthetic gradient retention data. Two distinct challenges in fitting gradient retention data were identified: 1) a lack of 'uniqueness' in the parameters and 2) an inability to find the global optimum fit in a complex fitting landscape. Working with experimental data where measurement noise is unavoidable will only make the accuracy worse.