Literature DB >> 22000780

Cross-column prediction of gas-chromatographic retention of polychlorinated biphenyls by artificial neural networks.

Angelo Antonio D'Archivio1, Angela Incani, Fabrizio Ruggieri.   

Abstract

In this paper, we build a multiple-column retention model able to predict the behaviour of polychlorinated biphenyls (PCBs) in capillary gas-chromatography (GC) within a wide range of separation conditions. To this end, GC retention is related to both chemical structure of PCBs, encoded by selected theoretical molecular descriptors, and the kind of stationary phase, represented by the relative retention time (RRT) of a suitable small number of analytes. The model was generated using the retention data of 70 PCBs extracted from the pool of the 209 possible congeners collected on 17 different capillary columns featured by non-polar or moderately polar stationary phases, reported in the literature. Multilinear regression combined with genetic algorithm variable selection was preliminarily applied to generate a four-dimensional quantitative structure-retention relationship (QSRR) for each of the 17 columns, based on theoretical molecular descriptors extracted from the large set provided by the software Dragon. 33 molecular descriptors obtained by merging the non-common descriptors of various single-column QSRRs, combined with RRTs values of the less and the most retained PCB, were considered as the starting independent variables of the multiple-column retention model. A multi-layer artificial neural network (ANN), optimised on a validation set extracted from the calibration data, was applied to generate the multi-column retention model. The influence of starting inputs on the network output was evaluated by a sensitivity analysis and model complexity was reduced through a step-wise elimination of redundant molecular descriptors, while RRTs of further PCBs were included to improve description of the stationary phase. Nine molecular descriptors and RRTs of eight selected PCBs are considered as the independent variables of the final ANN-based model, whose predictive performance was tested on the 139 PCBs excluded from calibration and on six external columns and/or temperature programs. Copyright Â
© 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 22000780     DOI: 10.1016/j.chroma.2011.09.071

Source DB:  PubMed          Journal:  J Chromatogr A        ISSN: 0021-9673            Impact factor:   4.759


  2 in total

1.  Retention Modelling of Phenoxy Acid Herbicides in Reversed-Phase HPLC under Gradient Elution.

Authors:  Alessandra Biancolillo; Maria Anna Maggi; Sebastian Bassi; Federico Marini; Angelo Antonio D'Archivio
Journal:  Molecules       Date:  2020-03-11       Impact factor: 4.411

2.  UHPLC Analysis of Saffron (Crocus sativus L.): Optimization of Separation Using Chemometrics and Detection of Minor Crocetin Esters.

Authors:  Angelo Antonio D'Archivio; Francesca Di Donato; Martina Foschi; Maria Anna Maggi; Fabrizio Ruggieri
Journal:  Molecules       Date:  2018-07-25       Impact factor: 4.411

  2 in total

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