Hydrophilic interaction ultra performance liquid chromatography retention prediction under gradient elution

The development and application of new separation mechanisms such as hydrophilic interaction chromatography (HILIC) is of high importance for the simultaneous analysis of polar molecules such as primary metabolites. However the retention mechanism in HILIC is not fully understood and as a result ret...

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Bibliographic Details
Published in:Analytical and bioanalytical chemistry 2012-08, Vol.404 (3), p.701-709
Main Authors: Gika, Helen, Theodoridis, Georgios, Mattivi, Fulvio, Vrhovsek, Urska, Pappa-Louisi, Adriani
Format: Article
Language:English
Subjects:
Algorithms
Amino acids
Amino Acids - chemistry
Analysis
Analytical Chemistry
Biochemistry
Characterization and Evaluation of Materials
Chemistry
Chemistry and Materials Science
Chromatography
Chromatography, High Pressure Liquid - methods
Construction
Elution
Food Science
Hydrophobic and Hydrophilic Interactions
Laboratory Medicine
Liquid chromatography
Mathematical analysis
Mathematical models
Models, Chemical
Monitoring/Environmental Analysis
Oligosaccharides - chemistry
Original Paper
Plant metabolites
Polyamines - chemistry
Separation
Utilities
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Summary:The development and application of new separation mechanisms such as hydrophilic interaction chromatography (HILIC) is of high importance for the simultaneous analysis of polar molecules such as primary metabolites. However the retention mechanism in HILIC is not fully understood and as a result retention prediction tools are not at hand for this chromatographic approach. In the present report we study the utility of a simple algorithm, based on a simple linear and/or a simple logarithmic retention model, for retention prediction in HILIC gradient separation of a mixture of 23 selected compounds including (poly)amines, amino acids, saccharides, and other molecules. Utilizing two types of gradient elution programs with or without an isocratic part, retention data were collected in order to build prediction models. Starting from at least three gradient runs the prediction of analyte retention was very satisfactory for all gradient programs tested, providing useful evidence of the value of such retention time prediction methodologies. Figure  
ISSN:1618-2642
1618-2650
DOI:10.1007/s00216-012-6015-6