Literature DB >> 29037590

Error measures in quantitative structure-retention relationships studies.

Maryam Taraji1, Paul R Haddad2, Ruth I J Amos1, Mohammad Talebi1, Roman Szucs3, John W Dolan4, Christopher A Pohl5.   

Abstract

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. Crown
Copyright © 2017. Published by Elsevier B.V. All rights reserved.

Keywords:  External validation; Prediction error measures; QSRR modelling; Root mean squared error of prediction

Mesh:

Year:  2017        PMID: 29037590     DOI: 10.1016/j.chroma.2017.09.050

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


  2 in total

1.  Prediction of Chromatographic Elution Order of Analytical Mixtures Based on Quantitative Structure-Retention Relationships and Multi-Objective Optimization.

Authors:  Petar Žuvela; J Jay Liu; Ming Wah Wong; Tomasz Bączek
Journal:  Molecules       Date:  2020-07-06       Impact factor: 4.411

2.  Quantitative Structure-Retention Relationships with Non-Linear Programming for Prediction of Chromatographic Elution Order.

Authors:  J Jay Liu; Alham Alipuly; Tomasz Bączek; Ming Wah Wong; Petar Žuvela
Journal:  Int J Mol Sci       Date:  2019-07-12       Impact factor: 5.923

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.