Barron, Leon and McEneff, Gillian ORCID: https://orcid.org/0000-0003-2590-413X (2016) Gradient liquid chromatographic retention time prediction for suspect screening applications: a critical assessment of a generalised artificial neural network-based approach across 10 multi-residue reversed-phase analytical methods. Talanta, 147. pp. 261-270. ISSN 0039-9140

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Abstract

For the first time, the performance of a generalised artificial neural network (ANN) approach for the prediction of 2492 chromatographic retention times (tR) is presented for a total of 1117 chemically diverse compounds present in a range of complex matrices and across 10 gradient reversed-phase liquid chromatography-(high resolution) mass spectrometry methods. Probabilistic, generalised regression, radial basis function as well as 2- and 3-layer multilayer perceptron-type neural networks were investigated to determine the most robust and accurate model for this purpose. Multi-layer perceptrons most frequently yielded the best correlations in 8 out of 10 methods. Averaged correlations of predicted versus measured tR across all methods were R2=0.918, 0.924 and 0.898 for the training, verification and test sets respectively. Predictions of blind test compounds (n=8–84 cases) resulted in an average absolute accuracy of 1.02±0.54 min for all methods. Within this variation, absolute accuracy was observed to marginally improve for shorter runtimes, but was found to be relatively consistent with respect to analyte retention ranges (~5%). Finally, optimised and replicated network dependency on molecular descriptor data is presented and critically discussed across all methods. Overall, ANNs were considered especially suitable for suspects screening applications and could potentially be utilised in bracketed-type analyses in combination with high resolution mass spectrometry.

Item Type:	Article
Identifier:	10.1016/j.talanta.2015.09.065
Keywords:	Retention time prediction; Non-target analysis; Suspect screening; High resolution mass spectrometry; Emerging contaminants
Subjects:	Law and criminal justice > Criminal justice > Forensic science
Depositing User:	Gillian McEneff
Date Deposited:	09 Aug 2017 17:41
Last Modified:	06 Feb 2024 15:53
URI:	https://repository.uwl.ac.uk/id/eprint/3695

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