Measuring and using scanning-gradient data for use in method optimization for liquid chromatography

Mimi J. den Uijl

doi:10.1016/j.chroma.2020.461780

2021-01-01

Mimi J. den Uijl *

Peter J. Schoenmakers

Grace K. Schulte

Dwight R. Stoll

Maarten R. van Bommel

Bob W.J. Pirok

DOI：10.1016/j.chroma.2020.461780

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摘要

En 中文

The use of scanning gradients can significantly reduce method-development time in reversed-phase liquid chromatography. However, there is no consensus on how they can best be used. In the present work we set out to systematically investigate various factors and to formulate guidelines. Scanning gradients are used to establish retention models for individual analytes. Different retention models were compared by computing the Akaike information criterion and the prediction accuracy. The measurement uncertainty was found to influence the optimum choice of model. The use of a third parameter to account for nonlinear relationships was consistently found not to be statistically significant. The duration (slope) of the scanning gradients was not found to influence the accuracy of prediction. The prediction error may be reduced by repeating scanning experiments or - preferably - by reducing the measurement uncertainty. It is commonly assumed that the gradient-slope factor, i.e. the ratio between slopes of the fastest and the slowest scanning gradients, should be at least three. However, in the present work we found this factor less important than the proximity of the slope of the predicted gradient to that of the scanning gradients. Also, interpolation to a slope between that of the fastest and the slowest scanning gradient is preferable to extrapolation. For comprehensive two-dimensional liquid chromatography (LC x LC) our results suggest that data obtained from fast second-dimension gradients cannot be used to predict retention in much slower first-dimension gradients. (C) 2020 The Authors. Published by Elsevier B.V.

Keyword:

Retention prediction

Scouting techniques

Method optimization, Retention modelling

Method development

Gradient elution