Brau Tyler, Pirok Bob, Rutan Sarah, Stoll Dwight
Department of Chemistry, Gustavus Adolphus College, 800 West College Avenue, Saint Peter, MN, 56082, USA.
Van't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, 1090 GD, The Netherlands.
J Sep Sci. 2022 Sep;45(17):3241-3255. doi: 10.1002/jssc.202100911. Epub 2022 Apr 4.
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.
在液相色谱中，在广泛的等度洗脱条件下拥有保留因子k的准确模型通常非常有用。原则上，保留模型的参数可以通过拟合等度或梯度保留因子数据来获得。然而，尽管我们自己多次尝试使用基于梯度保留数据训练的保留模型来准确预测等度k值，但在我们手中这并没有成功。在本研究中，我们使用了反相液相色谱条件下小分子的合成等度和梯度保留数据。这使我们能够发现与预测等度k值相关的挑战，而不受难以建模或消除的实验问题的混杂影响。结果表明，即使使用合成梯度保留数据，目前也无法始终如一地以优于10%的精度预测小分子的等度保留因子。确定了拟合梯度保留数据时的两个明显挑战：1）参数缺乏“唯一性”，2）在复杂的拟合环境中无法找到全局最优拟合。处理不可避免存在测量噪声的实验数据只会使精度更差。
