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Abstract

Quantitative Structure-Retention Relationship (QSRR) methodology is a useful tool in chromatography of all kinds, allowing the prediction of analyte retention time and providing insight into the mechanisms of separation. The prediction of retention is useful in reducing method development time and identifying analytes in Non-Targeted Analysis. The varying methods used for geometry optimization, descriptor calculation, feature selection, and model generation in many different QSRR settings are investigated and compared. It is found that the method of geometry optimization and descriptor selection is of less importance than the chromatographic similarity of compounds in the training sets used for model building in order to reduce the error of the model.

Item Type:	Article
Authors/Creators:	Amos, RIJ and Haddad, PR and Szucs, R and Dolan, JW and Pohl, CA
Keywords:	QSRR, geometry optimization, descriptor generation, chiral descriptors, feature selection, retention prediction, similarity, Non-Targeted Analysis, chromatography, uantitative structure-retention relationships
Journal or Publication Title:	Trends in Analytical Chemistry
Publisher:	Elsevier Science London
ISSN:	0165-9936
DOI / ID Number:	10.1016/j.trac.2018.05.019
Copyright Information:	Copyright 2018 Elsevier B.V.
Related URLs:	UTAS Profile: Amos, RIJ UTAS Profile: Haddad, PR
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