Pirok Bob W J, Pous-Torres Sandra, Ortiz-Bolsico Cassandra, Vivó-Truyols Gabriel, Schoenmakers Peter J
University of Amsterdam, van't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group, Science Park 904, 1098 XH Amsterdam, The Netherlands; TI-COAST, Science Park 904, 1098 XH Amsterdam, The Netherlands.
University of Amsterdam, van't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group, Science Park 904, 1098 XH Amsterdam, The Netherlands; Departament de Química Analítica, Universitat de València, c/Dr. Moliner 50, Burjassot 46100, Spain.
J Chromatogr A. 2016 Jun 10;1450:29-37. doi: 10.1016/j.chroma.2016.04.061. Epub 2016 Apr 29.
The challenge of fully optimizing LC×LC separations is horrendous. Yet, it is essential to address this challenge if sophisticated LC×LC instruments are to be utilized to their full potential in an efficient manner. Currently, lengthy method development is a major obstacle to the proliferation of the technique, especially in industry. A program was developed for the rigorous optimization of LC×LC separations, using gradient-elution in both dimensions. The program establishes two linear retention models (one for each dimension) based on just two LC×LC experiments. It predicts LC×LC chromatograms using a simple van-Deemter model to generalize band-broadening. Various objectives (analysis time, resolution, orthogonality) can be implemented in a Pareto-optimization framework to establish the optimal conditions. The program was successfully applied to a separation of a complex mixture of 54 aged, authentic synthetic dyestuffs, separated by ion-exchange chromatography and ion pair chromatography. The main limitation experienced was the retention-time stability in the first (ion-exchange) dimension. Using the PIOTR program LC×LC method development can be greatly accelerated, typically from a few months to a few days.
要全面优化二维液相色谱（LC×LC）分离极具挑战性。然而，如果要高效地充分发挥先进的LC×LC仪器的潜力，应对这一挑战至关重要。目前，漫长的方法开发是该技术推广的主要障碍，尤其是在工业领域。已开发出一个程序，用于严格优化LC×LC分离，在两个维度上均采用梯度洗脱。该程序仅基于两个LC×LC实验建立两个线性保留模型（每个维度一个）。它使用简单的范德姆特模型预测LC×LC色谱图以概括谱带展宽。各种目标（分析时间、分离度、正交性）可在帕累托优化框架中实现，以确定最佳条件。该程序已成功应用于分离54种老化的、真实的合成染料的复杂混合物，采用离子交换色谱和离子对色谱进行分离。遇到的主要限制是第一维（离子交换）中的保留时间稳定性。使用PIOTR程序，LC×LC方法开发可大幅加速，通常从几个月缩短至几天。
