4.6 Article

Improving peak shapes with counter gradients in two-dimensional high performance liquid chromatography

期刊

JOURNAL OF CHROMATOGRAPHY A
卷 1337, 期 -, 页码 147-154

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.chroma.2014.02.051

关键词

Two-dimensional high performance liquid; chromatography; Natural product separations; Column efficiency; Solvent strength; Counter gradient

资金

  1. Australian Research Council linkage grant [LP130100681]
  2. Alfred Deakin postdoctoral research fellowship (PGS)
  3. Deakin University postgraduate research award (DNB)
  4. Australian Research Council [LP130100681] Funding Source: Australian Research Council

向作者/读者索取更多资源

To achieve the greatest peak capacity in two-dimensional high performance liquid chromatography (2DHPLC) a gradient should be operated in both separation dimensions. However, it is known that when an injection solvent that is stronger than the initial mobile phase composition is deleterious to peak performance, thus causing problems when cutting a portion from one gradient into another. This was overcome when coupling hydrophilic interaction with reversed phase chromatography by introducing a counter gradient that changed the solvent strength of the second dimension injection. It was found that an injection solvent composition of 20% acetonitrile in water gave acceptable results in one-dimensional simulations with an initial composition of 5% acetonitrile. When this was transferred to a 2D-HPLC separation of standards it was found that a marked improvement in peak shape was gained for the moderately retained analytes (phenol and dimethyl phthalate), some improvement for the weakly retained caffeine and very little change for the strongly retained n-propylbenzene and anthracene which already displayed good chromatographic profiles. This effect was transferred when applied to a 2D-HPLC separation of a coffee extract where the indecipherable retention profile was transformed to a successful application multidimensional chromatography with peaks occupying 71% of the separation space according to the geometric approach to factor analysis. (C) 2014 Elsevier B.V. All rights reserved.

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