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Open AccessArticle

Wavelength Selection for NIR Spectroscopy Based on the Binary Dragonfly Algorithm

by Yuanyuan Chen 1,2,* and Zhibin Wang 2,3
1
School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
2
Engineering Technology Research Center of Shanxi Province for Opto-Electronic Information and Instrument, North University of China, Taiyuan 030051, China
3
School of Science, North University of China, Taiyuan 030051, China
*
Author to whom correspondence should be addressed.
Academic Editor: Christian Huck
Molecules 2019, 24(3), 421; https://doi.org/10.3390/molecules24030421
Received: 29 December 2018 / Revised: 19 January 2019 / Accepted: 22 January 2019 / Published: 24 January 2019
Wavelength selection is an important preprocessing issue in near-infrared (NIR) spectroscopy analysis and modeling. Swarm optimization algorithms (such as genetic algorithm, bat algorithm, etc.) have been successfully applied to select the most effective wavelengths in previous studies. However, these algorithms suffer from the problem of unrobustness, which means that the selected wavelengths of each optimization are different. To solve this problem, this paper proposes a novel wavelength selection method based on the binary dragonfly algorithm (BDA), which includes three typical frameworks: single-BDA, multi-BDA, ensemble learning-based BDA settings. The experimental results for the public gasoline NIR spectroscopy dataset showed that: (1) By using the multi-BDA and ensemble learning-based BDA methods, the stability of wavelength selection can improve; (2) With respect to the generalized performance of the quantitative analysis model, the model established with the wavelengths selected by using the multi-BDA and the ensemble learning-based BDA methods outperformed the single-BDA method. The results also indicated that the proposed method is not limited to the dragonfly algorithm but can also be combined with other swarm optimization algorithms. In addition, the ensemble learning idea can be applied to other feature selection areas to obtain more robust results. View Full-Text
Keywords: wavelength selection; NIR spectroscopy; binary dragonfly algorithm; ensemble learning; quantitative analysis modeling wavelength selection; NIR spectroscopy; binary dragonfly algorithm; ensemble learning; quantitative analysis modeling
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MDPI and ACS Style

Chen, Y.; Wang, Z. Wavelength Selection for NIR Spectroscopy Based on the Binary Dragonfly Algorithm. Molecules 2019, 24, 421.

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