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Beluga Optimization Algorithm for Near-Infrared Spectral Variable Selection of Complex Samples.

Javaria Kousar1, Liping Yang1, Jiale Xiang2

  • 1State Key Laboratory of Advanced Separation Membrane Materials, School of Chemical Engineering and Technology, Tiangong University, Tianjin 300387, China.

Foods (Basel, Switzerland)
|December 30, 2025
PubMed
Summary
This summary is machine-generated.

Discretized beluga whale optimization (DBWO) with partial least squares (PLS) improved quantitative analysis of complex samples. This spectral analysis method, DBWO-PLS, outperformed other variable selection techniques for enhanced predictive accuracy.

Keywords:
beluga whale optimizationdiscretizationpartial least squaresspectral analysisvariable selection

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Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Spectroscopy

Background:

  • Near-infrared (NIR) spectroscopy is crucial for quantitative analysis of complex samples.
  • High-dimensional spectral data often contains redundancy, necessitating variable selection for accurate modeling.
  • Optimization algorithms are vital for improving the efficiency and accuracy of multivariate calibration.

Purpose of the Study:

  • To evaluate the effectiveness of the discretized beluga whale optimization (DBWO) algorithm for variable selection in NIR spectral quantitative analysis.
  • To compare the predictive performance of DBWO combined with partial least squares (PLS) against other established variable selection methods.
  • To demonstrate the application of DBWO-PLS for analyzing complex samples like wheat, tablets, and cocoa beans.

Main Methods:

  • Implemented the discretized beluga whale optimization (DBWO) algorithm for variable selection.
  • Developed partial least squares (PLS) models using selected variables.
  • Compared DBWO-PLS with full-spectrum PLS, randomization test (RT)-PLS, uninformative variable elimination (UVE)-PLS, and Monte Carlo uninformative variable elimination (MC-UVE)-PLS.
  • Optimized DBWO parameters including the number of iterations and transfer function.

Main Results:

  • All tested variable selection methods (DBWO, RT, UVE, MC-UVE) improved the predictive accuracy of PLS models compared to full-spectrum analysis.
  • The DBWO-PLS model demonstrated superior predictive performance across wheat, tablet, and cocoa bean sample analyses.
  • DBWO exhibited fast convergence, high accuracy, and required minimal parameters, making it efficient for spectral data analysis.

Conclusions:

  • The discretized beluga whale optimization algorithm is a highly effective method for variable selection in NIR spectroscopic quantitative analysis.
  • DBWO-PLS offers a robust and accurate approach for analyzing complex samples, outperforming conventional methods.
  • This study highlights the potential of bio-inspired optimization algorithms in advancing chemometric modeling for spectral data.