Simple algorithms for fitting and optimisation for multilinear gradient elution in reversed-phase liquid chromatography

The theory of the multilinear gradient elution in reversed-phase liquid chromatography (RP-HPLC) presented in [P. Nikitas, A. Pappa-Louisi, P. Agrafiotou, J. Chromatogr. A 1120 (2006) 299] is modified to increase its flexibility. In addition, it is embodied to simple algorithms for fitting gradient...

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Bibliographic Details
Published in:Journal of Chromatography A Vol. 1157; no. 1; pp. 178 - 186
Main Authors: Nikitas, P., Pappa-Louisi, A., Papageorgiou, A.
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 20.07.2007
Elsevier
Subjects:
Algorithms
Analytical chemistry
Chemistry
Chromatographic methods and physical methods associated with chromatography
Chromatography, Liquid - methods
Exact sciences and technology
Gradient elution
Liquid chromatography
Optimisation techniques
Other chromatographic methods
Gradient elution
Optimisation techniques
Liquid chromatography
Elution
Gradient
Chromatographic retention
HPLC chromatography
Retention time
Algorithm
Optimization
α-Aminoacid
Reversed phase chromatography
Ultraviolet detector
Curve fitting
ISSN:0021-9673
Online Access:Get full text
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Summary:The theory of the multilinear gradient elution in reversed-phase liquid chromatography (RP-HPLC) presented in [P. Nikitas, A. Pappa-Louisi, P. Agrafiotou, J. Chromatogr. A 1120 (2006) 299] is modified to increase its flexibility. In addition, it is embodied to simple algorithms for fitting gradient data and especially for resolution optimisation under multilinear gradient conditions. In particular, two new algorithms for fitting and one for optimisation are tested and compared with conventional algorithms. Their performance was examined using 13 o-phthalaldehyde (OPA) derivatives of amino acids with mobile phases modified by acetonitrile. It was found that the new proposed algorithms, a repeated application of the Levenberg–Marquardt (LM) method for fitting (R_LM) and a modified descent algorithm for optimisation (RND_D), in combination with the modified theory of the multilinear gradient elution can lead to high quality predictions of the retention times and optimisation results.
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ISSN:0021-9673
DOI:10.1016/j.chroma.2007.04.059