Artificial neural network prediction of multilinear gradient retention in reversed-phase HPLC: comprehensive QSRR-based models combining categorical or structural solute descriptors ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-02

AUTHORS

Angelo Antonio D’Archivio, Maria Anna Maggi, Fabrizio Ruggieri

ABSTRACT

A multilayer artificial neural network (ANN) is used to model the reversed-phase liquid chromatography retention times of 16 selected compounds, including purines, pyrimidines and nucleosides. The analysed data, taken from literature, were collected in acetonitrile-water eluents under the application of 16 different multilinear gradients. The parameters describing the gradient profile together with solute descriptors are considered as the independent variables of an ANN-based model providing the retention time as response. Categorical variables or, alternatively, a selected set of molecular descriptors of computational origin are adopted to represent the solutes. Network training, validation and testing are performed preliminarily using data of 12, 2 and 4 gradients, respectively and successively, to investigate model performance under more severe calibration conditions, with data of 9, 2 and 7 gradients. The proposed approach allows a quite accurate prediction of retention times of the target analytes in external multilinear gradients. Categorical variables can successfully represent the target solutes when the model is called to transfer retention data from calibration to external gradients. In particular, using a five-dimensional bit string to represent the analytes, mean errors on retention times are 2 and 3 % under the most and less favourable calibration conditions, respectively. A comparable performance is observed if the categorical variables are replaced by five molecular descriptors, selected by a genetic algorithm within a large set of structural variables of computational origin. More... »

PAGES

1181-1190

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00216-014-8317-3

DOI

http://dx.doi.org/10.1007/s00216-014-8317-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1036389563

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25395205


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