INTRODUCTION
Developing a liquid chromatography (LC) method is as much an art as it is a science. One might imagine a seasoned chromatography expert creating an optimal method from scratch based solely on the chemistries of the analytes and columns. Such ad hoc method development, however, requires years of experience and may not be feasible for less experienced chromatographers.
Newer practitioners might find it challenging to know where to start when developing a method to separate analytes of interest. Although several software packages are available for method development, they can be costly and rely on the same theoretical principles discussed in this article. Among the various retention models, the linear solvent strength (LSS) model is arguably the simplest to use and shows relatively low variability in model fitting across a broad range of column chemistries and LC modes (<5% error), compared to more comprehensive models.
THEORY
The LSS model proposes that the natural logarithm of the analyte retention factor is linearly related to the logarithm of the retention factor in 100% weak solvent (Kw), the analyte-specific term (S) which describes the analyte’s dependence on organic solvent concentration in the mobile phase (determined by measuring ln(k) under varying organic fractions and is dependent on the type of organic solvent), and the organic fraction at any point during the gradient (measured as 0-1 v/v). This relationship is given in Equation 1:
1: lnk = lnkw – S * p
CONCLUSION
Liquid chromatography (LC) method development combines both science and art, heavily relying on the chromatographer’s expertise. However, a fundamental understanding of the underlying principles enables practitioners to develop optimized methods for separating complex mixtures. By adopting an iterative approach, chromatographers can adjust the gradient steepness and other parameters to achieve the desired separation. This method allows for refining techniques and improving the quality of the resulting data. We hope that this approach helps chromatographers more easily develop or optimize their methods, leading to enhanced analytical results.
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