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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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A hybrid variable selection method combining Fisher's linear discriminant combined population analysis and an

Shuobo Chen1, Kang Du1, Baoming Shan1

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A new hybrid variable selection method improves near-infrared (NIR) spectroscopy for industrial composition measurement. This approach enhances model accuracy and reduces prediction errors for products like beer, corn, and diesel fuel.

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Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Spectroscopy

Background:

  • Accurate composition measurement is crucial for industrial process control.
  • Near-infrared (NIR) spectroscopy offers a rapid, non-destructive analytical technique.
  • Variable selection is essential for building robust chemometric models, especially with multicollinear spectral data.

Purpose of the Study:

  • To propose a novel hybrid variable selection method for near-infrared (NIR) spectroscopy-based model building.
  • To enhance the accuracy and reliability of composition measurements in industrial processes.
  • To address challenges posed by multicollinearity in spectral data.

Main Methods:

  • A double-layer variable selection strategy combining Fisher's linear discriminant combined population analysis (FCPA) and an improved binary cuckoo search algorithm (IBCS).
  • FCPA for initial rough localization of informative variable intervals, handling multicollinearity.
  • IBCS, enhanced with opposition-based learning (OBL) and jumping genes (JG), for fine selection of key variables, avoiding local optima.

Main Results:

  • The proposed FCPA-IBCS method demonstrated superior performance in variable selection compared to other methods.
  • Partial Least Squares (PLS) models built using FCPA-IBCS showed higher fitting accuracy.
  • Reduced prediction errors were observed for the calibration models predicting beer extract, corn protein/starch, and diesel fuel boiling point.

Conclusions:

  • The hybrid FCPA-IBCS variable selection method is effective for NIR spectroscopic analysis in industrial applications.
  • This approach improves the accuracy and robustness of chemometric models for composition measurement.
  • The method successfully navigates multicollinearity and optimizes variable selection for enhanced predictive performance.