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Can We Trust Score Plots?

Marta Bevilacqua1, Rasmus Bro1

  • 1Department of Food Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg, Denmark.

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Summary
This summary is machine-generated.

Score plots from calibration models can mislead classification and discriminant analysis. Replacing them with cross-validated score plots provides more reliable interpretations for partial least squares regression models.

Keywords:
overfittingscore plotsvalidation

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Area of Science:

  • Chemometrics
  • Data Analysis
  • Machine Learning

Background:

  • Component models like partial least squares regression are widely used in chemometrics.
  • Score plots are common for visualizing these models, especially in classification tasks.
  • Current practices for interpreting score plots in classification may lead to misinterpretations.

Purpose of the Study:

  • To evaluate the validity of score plot interpretation in component models for classification.
  • To investigate the reliability of partial least squares regression for discriminant analysis (PLS-DA) score plots.
  • To propose and validate an improved method for score plot visualization.

Main Methods:

  • Utilized examples and simulation studies to assess score plot validity.
  • Compared score plots generated from calibration models versus cross-validated models.
  • Focused on partial least squares regression and PLS-DA.

Main Results:

  • Score plots from standard calibration models can yield misleading interpretations in classification tasks.
  • Cross-validated score plots offer a more accurate representation of model performance.
  • The proposed method demonstrates improved reliability for discriminant analysis.

Conclusions:

  • Standard score plots from calibration models are not always suitable for classification tasks.
  • Cross-validation is essential for accurate interpretation of score plots in partial least squares regression and PLS-DA.
  • Adopting cross-validated score plots enhances the reliability of chemometric modeling for classification.