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Error measures in quantitative structure-retention relationships studies.

Maryam Taraji1, Paul R Haddad1, Ruth I J Amos1

  • 1Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, 7001, Australia.

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|October 18, 2017
PubMed
Summary
This summary is machine-generated.

This study compared four error measures for quantitative structure-retention relationships (QSRR) models. Percentage Root Mean Squared Error of Prediction (RMSEP) best estimates QSRR model predictive ability, showing the lowest outlier residuals.

Keywords:
External validationPrediction error measuresQSRR modellingRoot mean squared error of prediction

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Area of Science:

  • Chemometrics
  • Computational Chemistry
  • Analytical Chemistry

Background:

  • Quantitative Structure-Retention Relationship (QSRR) models are crucial for predicting analyte retention times in chromatography.
  • Accurate evaluation of QSRR model predictive performance is essential for reliable predictions.
  • Various error measures exist, but their comparative effectiveness for QSRR validation is not fully established.

Purpose of the Study:

  • To analyze and compare the utility of four common error measures for evaluating QSRR predictive ability.
  • To determine the most effective error metric for assessing the prediction of retention times for external test analytes.
  • To identify the best-performing error measure based on outlier analysis.

Main Methods:

  • Comparison of four error measures: Mean Absolute Error (MAE), Percentage Mean Absolute Error (PMAE), Root Mean Squared Error (RMSE), and Percentage Root Mean Squared Error (PRMSE).
  • Application of these measures to evaluate the predictive performance of QSRR models on external test sets.
  • Utilized the sum of squared residuals (SSR) of outliers to assess the error metrics' sensitivity to deviations.

Main Results:

  • All four error measures were analyzed and compared for their effectiveness in QSRR model validation.
  • Percentage Root Mean Squared Error of Prediction (RMSEP) demonstrated superior performance in estimating predictive ability.
  • RMSEP yielded the lowest SSR value (20.43), indicating better handling of outliers compared to other metrics.

Conclusions:

  • Percentage Root Mean Squared Error of Prediction (RMSEP) is the recommended error measure for validating QSRR models.
  • RMSEP provides a more robust assessment of predictive performance, especially concerning outliers in retention time predictions.
  • This finding aids in selecting appropriate validation metrics for developing reliable QSRR models.