Improving retention-time prediction in supercritical-fluid chromatography by multivariate modelling

The prediction of chromatographic retention under supercritical-fluid chromatography (SFC) conditions was studied, using established and novel theoretical models over ranges of modifier content, pressure and temperature. Whereas retention models used for liquid chromatography often only consider the modifier fraction, retention in SFC depends much more strongly on pressure and temperature. The viability of combining several retention models into surfaces that describe the effects of both modifier fraction and pressure was investigated. The ability of commonly used retention models to describe retention as a function of modifier fraction, expressed either as mass or volume fraction, pressure and density was assessed. Using the multivariate surfaces, retention-time prediction for isocratic separations at constant temperature improved significantly compared to univariate modelling when both pressure and modifier fractions were changed. The mixed-mode model with an additional exponential pressure or density parameter was able to predict retention times within 5%, with the majority of the predictions within 2%. The use of mass fraction and density further improves retention modelling compared to volume fraction and pressure. These variables however, do require extra computations. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Automated summary

The prediction of chromatographic retention under supercritical-fluid chromatography conditions was studied using theoretical models. The study found that retention in SFC is strongly influenced by pressure and temperature, unlike liquid chromatography where retention models mainly consider the modifier fraction. The study explored the combination of multiple retention models to describe the effects of both modifier fraction and pressure. By using multivariate surfaces, retention-time prediction for isocratic separations at constant temperature improved significantly compared to univariate modeling when both pressure and modifier fractions were changed. The mixed-mode model with an additional exponential pressure or density parameter was able to predict retention times within 5%, with the majority of the predictions within 2%. The use of mass fraction and density further improved retention modeling compared to volume fraction and pressure, albeit requiring extra computations.