Characterisation of Gas-Chromatographic Poly(Siloxane) Stationary Phases by Theoretical Molecular Descriptors and Prediction of McReynolds Constants

Retention in gas-liquid chromatography is mainly governed by the extent of intermolecular interactions between the solute and the stationary phase. While molecular descriptors of computational origin are commonly used to encode the effect of the solute structure in quantitative structure-retention r...

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Bibliographic Details
Published in:International journal of molecular sciences 2019-04, Vol.20 (9), p.2120
Main Authors: D'Archivio, Angelo A, Giannitto, Andrea
Format: Article
Language:English
Subjects:
Chromatography
Datasets
Elution
Functional groups
Gas chromatography
Genetic algorithms
Investigations
McReynolds constants
molecular descriptors
Molecular structure
Neural networks
Organic chemistry
Polarity
poly(siloxane) stationary phases
Principal components analysis
QSRR modelling
retention prediction
Selectivity
Stationary phase
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Summary:Retention in gas-liquid chromatography is mainly governed by the extent of intermolecular interactions between the solute and the stationary phase. While molecular descriptors of computational origin are commonly used to encode the effect of the solute structure in quantitative structure-retention relationship (QSRR) approaches, characterisation of stationary phases is historically based on empirical scales, the McReynolds system of phase constants being one of the most popular. In this work, poly(siloxane) stationary phases, which occupy a dominant position in modern gas-liquid chromatography, were characterised by theoretical molecular descriptors. With this aim, the first five McReynolds constants of 29 columns were modelled by multilinear regression (MLR) coupled with genetic algorithm (GA) variable selection applied to the molecular descriptors provided by software Dragon. The generalisation ability of the established GA-MLR models, evaluated by both external prediction and repeated calibration/evaluation splitting, was better than that reported in analogous studies regarding nonpolymeric (molecular) stationary phases. Principal component analysis on the significant molecular descriptors allowed to classify the poly(siloxanes) according to their chemical composition and partitioning properties. Development of QSRR-based models combining molecular descriptors of both solutes and stationary phases, which will be applied to transfer retention data among different columns, is in progress.
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms20092120