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Int. J. Mol. Sci. 2012, 13(11), 15387-15400; doi:10.3390/ijms131115387

Predicting Retention Times of Naturally Occurring Phenolic Compounds in Reversed-Phase Liquid Chromatography: A Quantitative Structure-Retention Relationship (QSRR) Approach

1
Department of Chemistry, University of Sargodha, Sargodha 40100, Pakistan
2
Laboratory of Molecular Biomedicine, Institute of Bioscience, Universiti Putra Malaysia, Serdang 43400, Malaysia
*
Author to whom correspondence should be addressed.
Received: 25 September 2012 / Revised: 24 October 2012 / Accepted: 29 October 2012 / Published: 20 November 2012
(This article belongs to the Section Biochemistry, Molecular Biology and Biophysics)
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Abstract

Quantitative structure-retention relationships (QSRRs) have successfully been developed for naturally occurring phenolic compounds in a reversed-phase liquid chromatographic (RPLC) system. A total of 1519 descriptors were calculated from the optimized structures of the molecules using MOPAC2009 and DRAGON softwares. The data set of 39 molecules was divided into training and external validation sets. For feature selection and mapping we used step-wise multiple linear regression (SMLR), unsupervised forward selection followed by step-wise multiple linear regression (UFS-SMLR) and artificial neural networks (ANN). Stable and robust models with significant predictive abilities in terms of validation statistics were obtained with negation of any chance correlation. ANN models were found better than remaining two approaches. HNar, IDM, Mp, GATS2v, DISP and 3D-MoRSE (signals 22, 28 and 32) descriptors based on van der Waals volume, electronegativity, mass and polarizability, at atomic level, were found to have significant effects on the retention times. The possible implications of these descriptors in RPLC have been discussed. All the models are proven to be quite able to predict the retention times of phenolic compounds and have shown remarkable validation, robustness, stability and predictive performance. View Full-Text
Keywords: QSRR (quantitative structure-retention relationship); naturally occurring phenolic compounds; artificial neural networks; unsupervised forward selection; reversed phase liquid chromatography QSRR (quantitative structure-retention relationship); naturally occurring phenolic compounds; artificial neural networks; unsupervised forward selection; reversed phase liquid chromatography
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Akbar, J.; Iqbal, S.; Batool, F.; Karim, A.; Chan, K.W. Predicting Retention Times of Naturally Occurring Phenolic Compounds in Reversed-Phase Liquid Chromatography: A Quantitative Structure-Retention Relationship (QSRR) Approach. Int. J. Mol. Sci. 2012, 13, 15387-15400.

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