• Spectroscopy and Spectral Analysis
  • Vol. 42, Issue 9, 2807 (2022)
Si-ying CHEN1、*, Yi-wen JIA1、1;, Yu-rong JIANG1、1; *;, He CHEN1、1;, Wen-hui YANG2、2;, Yu-peng LUO1、1;, Zhong-shi LI1、1;, Yin-chao ZHANG1、1;, and Pan GUO1、1;
Author Affiliations
  • 11. School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
  • 22. Academy of Military Medical Sciences, Academy of Military Sciences, Beijing 100071, China
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    DOI: 10.3964/j.issn.1000-0593(2022)09-2807-06 Cite this Article
    Si-ying CHEN, Yi-wen JIA, Yu-rong JIANG, He CHEN, Wen-hui YANG, Yu-peng LUO, Zhong-shi LI, Yin-chao ZHANG, Pan GUO. Classification and Recognition of Adulterated Manuka Honey by Multi-Wavelength Laser-Induced Fluorescence[J]. Spectroscopy and Spectral Analysis, 2022, 42(9): 2807 Copy Citation Text show less
    Schematic diagram of laser-induced fluorescence experiment system
    Fig. 1. Schematic diagram of laser-induced fluorescence experiment system
    Data processing flowchart
    Fig. 2. Data processing flowchart
    Fluorescence spectra after pretreatment===(a), (b), (c): 266 nm excitation; (d), (e), (f): 355 nm excitation; (g), (h), (i): 405 nm excitation; (j), (k), (l): 450 nm excitation;(a), (d), (g), (j): Sample A; (b), (e), (h), (k): Sample B; (c), (f), (i), (l): Sample C (the same below)
    Fig. 3. Fluorescence spectra after pretreatment===(a), (b), (c): 266 nm excitation; (d), (e), (f): 355 nm excitation; (g), (h), (i): 405 nm excitation; (j), (k), (l): 450 nm excitation;(a), (d), (g), (j): Sample A; (b), (e), (h), (k): Sample B; (c), (f), (i), (l): Sample C (the same below)
    Dimensionality reduction diagram of training set data
    Fig. 4. Dimensionality reduction diagram of training set data
    激发波长/nm分类算法ABC
    识别率/%标准差识别率/%标准差识别率/%标准差
    266KNN98.750.008 1100099.970.001 6
    355KNN92.770.027 793.620.018 098.200.013 8
    405KNN92.850.025 198.880.009 596.580.014 6
    450KNN57.520.044 065.480.029 257.420.047 8
    Table 1. Recognition rates and standard deviations of three kinds of honey under different excitation wavelengths
    激发波长
    /nm
    分类
    算法
    ABC
    识别率/%差值/%标准差识别率/%差值/%标准差识别率/%差值/%标准差
    266KNN
    SVM
    98.75
    97.18
    1.570.008 1
    0.014 4
    100
    98.87
    1.130
    0.0105
    99.97
    98.88
    1.090.001 6
    0.009 8
    355KNN
    SVM
    92.77
    92.10
    0.670.027 7
    0.034 0
    93.62
    93.48
    0.140.018 0
    0.018 3
    98.20
    96.80
    1.400.013 8
    0.016 4
    405KNN
    SVM
    92.85
    92.75
    0.100.025 1
    0.023 3
    98.88
    97.78
    1.100.009 5
    0.018 6
    96.58
    95.63
    0.950.014 6
    0.017 7
    Table 2. Recognition rates and standard deviations for different excitation wavelengths and classification algorithms
    Si-ying CHEN, Yi-wen JIA, Yu-rong JIANG, He CHEN, Wen-hui YANG, Yu-peng LUO, Zhong-shi LI, Yin-chao ZHANG, Pan GUO. Classification and Recognition of Adulterated Manuka Honey by Multi-Wavelength Laser-Induced Fluorescence[J]. Spectroscopy and Spectral Analysis, 2022, 42(9): 2807
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