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用硫酸化β-环糊精分离芳香族氨基酸对映体时的选择性控制

Selectivity control in the separation of aromatic amino acid enantiomers with sulphated beta-cyclodextrin.

作者信息

Zakaria Philip, Macka Miroslav, Haddad Paul R

机构信息

Australian Centre for Research on Separation Science, School of Chemistry, University of Tasmania, Private Bag 75, Hobart, Tasmania 7001, Australia.

出版信息

J Chromatogr A. 2004 Mar 26;1031(1-2):179-86. doi: 10.1016/j.chroma.2003.11.013.

DOI:10.1016/j.chroma.2003.11.013
PMID:15058581
Abstract

Control of selectivity in the enantiomeric separation of three aromatic amino acids (phenylalanine, tyrosine and tryptophan) is demonstrated by electrokinetic capillary chromatography utilising temperature variations coupled with the use of sulphated-beta-cyclodextrin (s-beta-CD) as a pseudostationary phase. The concentration of s-beta-CD and temperature were used as experimental variables to control the observed selectivity. A double-coated capillary was used and proved very robust with reproducibility of migration times being <2.0% R.S.D. between runs and <2.6% on using a new capillary. The system was modelled successfully using an artificial neural network (ANN) comprising one input layer, two hidden layers and one output layer. The model accurately described the observed separations with a correlation coefficient of 0.999 being observed between predicted and observed migration times. Selectivity optimisation was achieved using the normalised resolution product and minimum resolution criteria, with both providing optima at different experimental conditions. The selectivity changes observed also allowed the estimation of electrolyte temperatures within the capillary at high operating currents (>100 microA). Using a 50 microm i.d. capillary and an electrolyte comprising 20 mM phosphate and 15 mM s-beta-CD, a temperature of 52 degrees C was calculated within the capillary at an applied voltage of +30 kV.

摘要

通过电动毛细管色谱法,利用温度变化并结合使用硫酸化β-环糊精(s-β-CD)作为假固定相,证明了对三种芳香族氨基酸(苯丙氨酸、酪氨酸和色氨酸)对映体分离的选择性控制。将s-β-CD的浓度和温度用作实验变量,以控制观察到的选择性。使用了双涂层毛细管,结果证明其非常耐用,运行之间迁移时间的重现性<2.0%相对标准偏差,使用新毛细管时<2.6%。该系统使用包含一个输入层、两个隐藏层和一个输出层的人工神经网络(ANN)成功建模。该模型准确地描述了观察到的分离情况,预测迁移时间与观察到的迁移时间之间的相关系数为0.999。使用归一化分辨率乘积和最小分辨率标准实现了选择性优化,两者都在不同的实验条件下提供了最佳值。观察到的选择性变化还允许估计高工作电流(>100微安)下毛细管内的电解质温度。使用内径为50微米的毛细管和包含20 mM磷酸盐和15 mM s-β-CD的电解质,在+30 kV的施加电压下,计算出毛细管内的温度为52℃。

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