Vrije Universiteit Brussel, Department of Chemical Engineering, Pleinlaan 2, 1050, Brussel, Belgium.
University of Leuven (KU Leuven), Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, Herestraat 49, Leuven, Belgium.
J Chromatogr A. 2024 Dec 6;1738:465469. doi: 10.1016/j.chroma.2024.465469. Epub 2024 Oct 22.
An alternative strategy is explored for the separation of samples by liquid chromatography (LC). Unlike traditional approaches that aim to resolve all components in a given sample within a single LC separation, the proposed strategy uses two or more distinct separations carried out with a different gradient program and/or using different separation chemistries i.e., a different set of mobile and stationary phase. This set of complementary incomplete separations (CIS) is selected such that each component is at least fully resolved once, meaning the most critical pairs of each individual separation can be left unseparated. This allows for a significant time saving per separation. To investigate whether such an approach can lead to overall shorter analysis times than is possible with the fastest single-run gradient separation, a comprehensive in silico study covering a statistically significant number of samples is undertaken. The investigation shows that, for the presently considered sample sets and chemistries, CIS has a substantially higher probability, about two times greater for the simplest samples considered in this work and as much as 30 times greater for more complex samples, to fully resolve an unknown sample compared to a single gradient separation. Comparing separation speeds, the CIS approach can achieve complete sample resolution on average approximately four times faster than a single separation. Our findings thus demonstrate the potential of CIS in enhancing separation efficiency and offer insights regarding their use for solving analytical challenges.
一种用于液相色谱(LC)分离样品的替代策略被探索。与旨在在单次 LC 分离中解析给定样品中所有组分的传统方法不同,所提出的策略使用两种或更多种不同的分离,采用不同的梯度程序和/或使用不同的分离化学,即不同的流动相和固定相组合。选择这组互补不完全分离(CIS),使得每个组分至少能完全解析一次,这意味着可以留下每个单独分离中最关键的对不分离。这允许每个分离都能显著节省时间。为了研究这种方法是否可以导致整体分析时间比最快的单次梯度分离更短,进行了一项涵盖大量样本的综合计算机模拟研究。研究表明,对于目前考虑的样本集和化学物质,与单次梯度分离相比,CIS 具有更高的整体完全解析未知样本的概率,对于本工作中考虑的最简单的样本,概率约高出两倍,对于更复杂的样本,概率高达 30 倍。在比较分离速度时,CIS 方法可以将平均完全解析样本的速度提高约四倍。因此,我们的发现证明了 CIS 在提高分离效率方面的潜力,并提供了有关其用于解决分析挑战的见解。