Suppr超能文献

柱组合对全二维气相色谱中二维分离的影响:正交性评估及机理探究

Effect of column combinations on two-dimensional separation in comprehensive two-dimensional gas chromatography: estimation of orthogonality and exploring of mechanism.

作者信息

Zhu Shukui

机构信息

Department of Chemistry, College of Science, Huazhong Agricultural University, Wuhan, China.

出版信息

J Chromatogr A. 2009 Apr 10;1216(15):3312-7. doi: 10.1016/j.chroma.2009.01.107. Epub 2009 Feb 5.

Abstract

With the analysis of Chinese liquor Moutai as an example, the effect of different column combinations was studied on two-dimensional separation in comprehensive two-dimensional gas chromatography (GCxGC). A method to optimize column combinations was developed for achieving maximum orthogonality. Using a geometric approach to factor analysis, the degree of separation orthogonality was quantitatively estimated. The parameters evaluated include peak spreading angle, retention correlation, and practical peak capacity. When using the "reversed-type" column combinations (a polar column as the first dimension and a non- or less polar one as the second dimension), correlation coefficient was lower than or equal to 0.221, the spreading angle was higher than or equal to 77 degrees , and more than 92% of the theoretical peak capacity was reasonably used. For Moutai liquor mainly consisting of some polar compounds, the HP-Innowax+DB1701 column combination was optimal. In addition, through the test of Grob mixture and McReynolds constant, the mechanism of solute-stationary phase interactions was disclosed in details, which validated the estimation of GCxGC orthogonality in a molecular level.

摘要

以中国白酒茅台的分析为例,研究了不同柱组合在全二维气相色谱(GCxGC)二维分离中的效果。开发了一种优化柱组合的方法以实现最大正交性。采用几何因子分析方法,对分离正交性程度进行了定量评估。评估的参数包括峰扩展角、保留相关性和实际峰容量。当使用“反转型”柱组合(第一维为极性柱,第二维为非极性或弱极性柱)时,相关系数小于或等于0.221,扩展角大于或等于77度,且理论峰容量的92%以上得到合理利用。对于主要由一些极性化合物组成的茅台酒,HP-Innowax+DB1701柱组合是最佳的。此外,通过Grob混合物和麦克雷诺兹常数测试,详细揭示了溶质-固定相相互作用的机制,这在分子水平上验证了GCxGC正交性的评估。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验