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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Modeling power-based variable selection for rigorous one-class classification with SIMCA.

Mateus Pires Schneider1, Cristina Malegori2, Paolo Oliveri2

  • 1Institute of Chemistry, UFRGSAv. Bento Gonçalves, 9500, Porto Alegre, RS, CEP 91591 - 970, Brazil.

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|September 29, 2025
PubMed
Summary
This summary is machine-generated.

A new algorithm selects key variables for Soft Independent Modeling of Class Analogy (SIMCA) in one-class classification (OCC). This method enhances model interpretability and parsimony without sacrificing classification performance in food authentication.

Keywords:
ChemometricsFood authenticationModeling powerOne-class classificationSIMCASpectroscopyVariable selection

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Area of Science:

  • Chemometrics
  • Machine Learning
  • Analytical Chemistry

Background:

  • One-class classification (OCC) is crucial for food authentication when only target samples are available.
  • Traditional Soft Independent Modeling of Class Analogy (SIMCA) often uses full spectral or chromatographic data, leading to complex models.
  • Model parsimony and interpretability are key challenges in developing robust classification models.

Purpose of the Study:

  • To develop and validate a variable selection algorithm for SIMCA.
  • To improve model parsimony and interpretability in OCC.
  • To assess the performance of the proposed algorithm against traditional SIMCA.

Main Methods:

  • A novel variable selection algorithm, Modeling Power Selector with SIMCA (MPS-SIMCA), was developed.
  • The algorithm integrates three criteria: MP-compactness correlation, MP non-growth rate, and minimum MP threshold.
  • The algorithm was tested on UV-Vis, NIR, and HPLC-CAD datasets from edible oils, green teas, and olive oils.

Main Results:

  • MPS-SIMCA achieved equivalent or improved classification performance compared to full-spectrum SIMCA.
  • The selected variables were chemically meaningful and aligned with known compositional markers.
  • MPS-SIMCA demonstrated superior model compactness and interpretability.

Conclusions:

  • Variable selection based on internal class structure is feasible for SIMCA.
  • The MPS-SIMCA algorithm provides a robust and interpretable approach for food authentication.
  • This method enhances the practical application of SIMCA in real-world scenarios with limited sample availability.