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Error measures in quantitative structure-retention relationships studies


Taraji, M and Haddad, PR and Amos, RIJ and Talebi, M and Szucs, R and Dolan, JW and Pohl, CA, Error measures in quantitative structure-retention relationships studies, Journal of Chromatography A, 1524 pp. 298-302. ISSN 0021-9673 (2017) [Refereed Article]

Copyright Statement

Copyright 2017 Crown Copyright. Published by Elsevier B.V.

DOI: doi:10.1016/j.chroma.2017.09.050


An analysis and comparison of the use of four commonly used error measures (mean absolute error, percentage mean absolute error, root mean square error, and percentage root mean square error) for evaluating the predictive ability of quantitative structure-retention relationships (QSRR) models is reported. These error measures are used for reporting errors in the prediction of retention time of external test analytes, that is, analytes not employed during model development. The error-based validation metrics were compared using a simple descriptive statistic, the sum of squared residuals (SSR) of outliers to the edge of an error window. The comparisons demonstrate that Percentage Root Mean Squared Error of Prediction (RMSEP) provides the best estimate of the predictive ability of a QSRR model, having the lowest SSR value of 20.43.

Item Details

Item Type:Refereed Article
Keywords:QSRR modelling, external validation, prediction error measures, root mean squared error of prediction
Research Division:Chemical Sciences
Research Group:Analytical chemistry
Research Field:Separation science
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the chemical sciences
UTAS Author:Taraji, M (Ms Maryam Taraji)
UTAS Author:Haddad, PR (Professor Paul Haddad)
UTAS Author:Amos, RIJ (Dr Ruth Amos)
UTAS Author:Talebi, M (Dr Mohammad Talebi)
ID Code:122432
Year Published:2017
Funding Support:Australian Research Council (LP120200700)
Web of Science® Times Cited:22
Deposited By:Austn Centre for Research in Separation Science
Deposited On:2017-11-14
Last Modified:2022-08-22

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