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首页> 外文期刊>Journal of chromatography, A: Including electrophoresis and other separation methods >Accurate modelling of the retention behaviour of peptides in gradient-elution hydrophilic interaction liquid chromatography
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Accurate modelling of the retention behaviour of peptides in gradient-elution hydrophilic interaction liquid chromatography

机译:梯度洗脱亲水相互作用液相色谱中肽保留行为的准确建模

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摘要

The applicability of models to describe peptide retention in hydrophilic interaction liquid chromatography (HILIC) was investigated. A tryptic digest of bovine-serum-albumin (BSA) was used as a test sample. Several different models were considered, including adsorption, mixed-mode, exponential, quadratic and Neue-Kuss models. Gradient separations were performed on three different HILIC stationary-phases under three different mobile-phase conditions to obtain model parameters. Methods to track peaks for specific peptides across different chromatograms are shown to be essential. The optimal mobile-phase additive for the separation of BSA digest on each of the three columns was selected by considering the retention window, peak width and peak intensity with mass-spectrometric detection. The performance of the models was investigated using the Akaike information criterion (AIC) to measure the goodness-of-fit and evaluated using prediction errors. The F-test for regression was applied to support model selection. RPLC separations of the same sample,were used to test the models. The adsorption model showed the best performance for all the HILIC columns investigated and the lowest prediction errors for two of the three columns. In most cases prediction errors were within 1%. (C) 2019 The Authors. Published by Elsevier B.V.
机译:研究了模型来描述亲水性相互作用液相色谱(HILIC)的肽保留的适用性。使用牛血清白蛋白(BSA)的胰蛋白酶摘要作为测试样品。考虑了几种不同的模型,包括吸附,混合模式,指数,二次和Neue-Kuss模型。在三种不同的移动相条件下对三个不同的HILIC固定相进行梯度分离,以获得模型参数。在不同色谱图中跟踪特异性肽峰的方法是必不可少的。通过考虑保持窗口,峰值宽度和峰强度,通过考虑具有质谱检测的保留窗口,峰值宽度和峰值强度,选择用于分离BSA消解的最佳流动阶段添加剂。使用Akaike信息标准(AIC)来研究模型的性能来测量使用预测误差的拟合的优度和评估。应用回归的F检验以支持模型选择。使用相同样品的RPLC分离,用于测试模型。吸附模型对所有HILIC列的最佳性能显示了所有研究的所有HILIC列和三列中两个的最低预测误差。在大多数情况下,预测误差在1%以内。 (c)2019年作者。由elsevier b.v出版。

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