Laser & Optoelectronics Progress, Volume. 57, Issue 20, 203001(2020)

Application of Kernel Extreme Learning Machine and Laser Induction Fluorescence Technique in Edible Oil Identification

Mengran Zhou, Jinguo Wang*, Hongping Song, Feng Hu, Wenhao Lai, and Kai Bian
Author Affiliations
  • College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, Anhui 232001, China
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    References(15)

    [5] Bi X, Jin Y B, Li S F et al. Rapid and sensitive determination of fatty acids in edible oil by liquid chromatography-electrospray ionization tandem mass spectrometry[J]. Science China Chemistry, 57, 447-452(2014).

    [6] Chen Y, Luo Q S, Wang J et al. Rapid identification and characterization of recovered edible oil, based on Raman and near-infrared spectroscopy[C]//Proceedings of the 2018 3rd International Conference on Modelling. Paris,, 4(2018).

    [11] Wang Y, Zhou M R, Yan P C et al. A rapid identification model of mine water inrush based on extreme learning machine[J]. Journal of China Coal Society, 42, 2427-2432(2017).

    [13] Mirjalili S. Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm[J]. Knowledge-Based Systems, 89, 228-249(2015).

    [14] Deng C W, Huang G B, Xu J et al. Extreme learning machines: new trends and applications[J]. Science China Information Sciences, 58, 1-16(2015).

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    Mengran Zhou, Jinguo Wang, Hongping Song, Feng Hu, Wenhao Lai, Kai Bian. Application of Kernel Extreme Learning Machine and Laser Induction Fluorescence Technique in Edible Oil Identification[J]. Laser & Optoelectronics Progress, 2020, 57(20): 203001

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    Paper Information

    Category: Spectroscopy

    Received: Dec. 4, 2019

    Accepted: Jan. 9, 2020

    Published Online: Oct. 17, 2020

    The Author Email: Jinguo Wang (wangjinguo1023@163.com)

    DOI:10.3788/LOP57.203001

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