Toward the General Mechanistic Model of Liquid Chromatographic Retention,Analytical Chemistry

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Toward the General Mechanistic Model of Liquid Chromatographic Retention
Analytical Chemistry ( IF 6.7 ) Pub Date : 2022-07-29 , DOI: 10.1021/acs.analchem.2c02034
Agnieszka Kamedulska ₁ , Łukasz Kubik ₁ , Julia Jacyna ₁ , Wiktoria Struck-Lewicka ₁ , Michał J Markuszewski ₁ , Paweł Wiczling ₁

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Large datasets of chromatographic retention times are relatively easy to collect. This statement is particularly true when mixtures of compounds are analyzed under a series of gradient conditions using chromatographic techniques coupled with mass spectrometry detection. Such datasets carry much information about chromatographic retention that, if extracted, can provide useful predictive information. In this work, we proposed a mechanistic model that jointly explains the relationship between pH, organic modifier type, temperature, gradient duration, and analyte retention based on liquid chromatography retention data collected for 187 small molecules. The model was built utilizing a Bayesian multilevel framework. The model assumes (i) a deterministic Neue equation that describes the relationship between retention time and analyte-specific and instrument-specific parameters, (ii) the relationship between analyte-specific descriptors (log P, pK_a, and functional groups) and analyte-specific chromatographic parameters, and (iii) stochastic components of between-analyte and residual variability. The model utilizes prior knowledge about model parameters to regularize predictions which is important as there is ample information about the retention behavior of analytes in various stationary phases in the literature. The usefulness of the proposed model in providing interpretable summaries of complex data and in decision making is discussed.

中文翻译：

液相色谱保留的一般机理模型

色谱保留时间的大型数据集相对容易收集。当使用色谱技术结合质谱检测在一系列梯度条件下分析化合物混合物时，这种说法尤其正确。此类数据集包含有关色谱保留的大量信息，如果提取这些信息，可以提供有用的预测信息。在这项工作中，我们提出了一个机制模型，根据收集的 187 个小分子的液相色谱保留数据，共同解释 pH、有机改性剂类型、温度、梯度持续时间和分析物保留之间的关系。该模型是利用贝叶斯多层框架构建的。该模型假设 (i) 确定性 Neue 方程描述保留时间与分析物特定和仪器特定参数之间的关系，(ii) 分析物特定描述符（log P、p Ka 和官能团 _）之间的关系以及分析物特定的色谱参数，以及 (iii) 分析物之间的随机成分和残余变异性。该模型利用有关模型参数的先验知识来规范预测，这一点很重要，因为文献中有大量有关分析物在各种固定相中的保留行为的信息。讨论了所提出的模型在提供复杂数据的可解释摘要和决策中的有用性。

更新日期：2022-07-29

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