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Wavelength Selection for NIR Spectroscopy Based on the Binary Dragonfly Algorithm.

Yuanyuan Chen1,2, Zhibin Wang3,4

  • 1School of Information and Communication Engineering, North University of China, Taiyuan 030051, China. chenyy@nuc.edu.cn.

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|January 27, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a robust wavelength selection method using the binary dragonfly algorithm (BDA) for near-infrared spectroscopy. Multi-BDA and ensemble learning frameworks enhance selection stability and improve quantitative analysis models.

Keywords:
NIR spectroscopybinary dragonfly algorithmensemble learningquantitative analysis modelingwavelength selection

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Area of Science:

  • Chemometrics
  • Spectroscopy
  • Machine Learning

Background:

  • Wavelength selection is crucial for near-infrared (NIR) spectroscopy analysis.
  • Existing swarm optimization algorithms lack robustness, leading to inconsistent wavelength selection.

Purpose of the Study:

  • To develop a novel and robust wavelength selection method for NIR spectroscopy.
  • To improve the stability and performance of quantitative analysis models using selected wavelengths.

Main Methods:

  • Proposed a wavelength selection method based on the binary dragonfly algorithm (BDA).
  • Investigated three frameworks: single-BDA, multi-BDA, and ensemble learning-based BDA.
  • Applied the methods to a public gasoline NIR spectroscopy dataset.

Main Results:

  • Multi-BDA and ensemble learning-based BDA significantly improved wavelength selection stability.
  • Models built with wavelengths from multi-BDA and ensemble BDA showed superior generalized performance compared to single-BDA.
  • The proposed method demonstrated potential for integration with other swarm optimization algorithms and feature selection applications.

Conclusions:

  • The binary dragonfly algorithm, particularly in multi-objective and ensemble learning configurations, offers a robust solution for wavelength selection in NIR spectroscopy.
  • This approach enhances model stability and predictive accuracy.
  • The ensemble learning concept can be broadly applied to improve robustness in various feature selection tasks.