Journal
ANALYTICA CHIMICA ACTA
Volume 602, Issue 2, Pages 164-172Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.aca.2007.09.014
Keywords
quantitative structure-retention relationships; molecular descriptors; retention prediction; reversed-phase chromatography
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In the literature an increasing interest in quantitative structure-retention relationships (QSRR) can be observed. After a short introduction on QSRR and other strategies proposed to deal with the starting point selection problem prior to method development in reversed-phase liquid chromatography, a number of interesting papers is reviewed, dealing with QSRR models for reversed-phase liquid chromatography The main focus in this review paper is put on the different modelling methodologies applied and the molecular descriptors used in the QSRR approaches. Besides two semi-quantitative approaches (i.e. principal component analysis, and decision trees), these methodologies include artificial neural networks, partial least squares, uninformative variable elimination partial least squares, stochastic gradient boosting for tree-based models, random forests, genetic algorithms, multivariate adaptive regression splines, and two-step multivariate adaptive regression splines. (C) 2007 Elsevier B.V All rights reserved.
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