Spectroscopy and Spectral Analysis, Volume. 42, Issue 4, 1299(2022)

Detection of Low Chromaticity Difference Foreign Matters in Soy Protein Meat Based on Hyperspectral Imaging Technology

Ji-yong SHI*... Chuan-peng LIU, Zhi-hua LI, Xiao-wei HUANG, Xiao-dong ZHAI, Xue-tao HU, Xin-ai ZHANG, Di ZHANG and Xiao-bo ZOU*; |Show fewer author(s)
Author Affiliations
  • School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
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    Figures & Tables(10)
    Low-chromatic aberration foreign matters and soy protein meat samples containing foreign matters
    (a) Hyperspectral imaging system; (b) Hyperspectral 3D data1: Computer; 2: Height adjustment lever; 3: Spectrometer; 4: Lens; 5: Dark box; 6: Stage; 7: Electronically controlled mobile platform; 8: Light source
    Detection flow chart of low-chromatic aberration foreign matters in soy protein meat
    Spectral/image features of artificial meat slices with foreign matters(a): Color images of soy protein meat with foreign matters; (b): R gray images of color images; (c): G gray images of color images; (d): B gray images of color images; (e): average spectral data of soy protein meat and foreign matters
    Scores of the first three principal components of principal component analysis
    Screening results of characteristic wavelengths(a): Root mean square error curve; (b): Schematic diagram of characteristic wavelengths
    Hyperspectral imaging technology and computer vision technology for foreign matter detection(a): Artificial meat slices mixed with foreign matters; (b): Areas of interest for artificial meat slices; (c): Gray image data of the region of interest; (d): Hyperspectral image data of the region of interest; (e): Binary image of foreign object segmentation by conventional computer vision; (f): binary image after image segmentation using the best foreign object recognition model
    • Table 1. LDA classification results of different spectral preprocessing methods

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      Table 1. LDA classification results of different spectral preprocessing methods

      预处理方法PCs校正集识别率/%预测集识别率/%
      SG395.0094.17
      SNVT795.0090.83
      MSC694.1792.5
      VN691.6785.83
      1st681.6779.17
      2nd492.592.5
    • Table 2. Foreign matters recognition rate of different pattern recognition models (%)

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      Table 2. Foreign matters recognition rate of different pattern recognition models (%)

      模型种类全波段特征波段主成分变量
      LDACal90.2893.8895.83
      Val89.1793.3395.00
      KNNCal84.1785.4286.39
      Val80.8381.6784.17
      BP-ANNCal93.3394.7297.50
      Val93.8895.8398.33
      SVMCal93.3394.1796.67
      Val92.5094.1795.83
    • Table 3. BP-ANN verification results of artificial meat containing foreign matters

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      Table 3. BP-ANN verification results of artificial meat containing foreign matters

      尺寸PCsIr/%TPFNTNFPSe/%Sp/%
      3×35964734919498
      5×559848250096100
      10×105974824919698
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    Ji-yong SHI, Chuan-peng LIU, Zhi-hua LI, Xiao-wei HUANG, Xiao-dong ZHAI, Xue-tao HU, Xin-ai ZHANG, Di ZHANG, Xiao-bo ZOU. Detection of Low Chromaticity Difference Foreign Matters in Soy Protein Meat Based on Hyperspectral Imaging Technology[J]. Spectroscopy and Spectral Analysis, 2022, 42(4): 1299

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    Paper Information

    Category: Research Articles

    Received: Dec. 4, 2020

    Accepted: --

    Published Online: Jul. 25, 2023

    The Author Email: SHI Ji-yong (shi_jiyong@ujs.edu.cn)

    DOI:10.3964/j.issn.1000-0593(2022)04-1299-07

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