• Spectroscopy and Spectral Analysis
  • Vol. 42, Issue 7, 2290 (2022)
Zhi-hao WANG*, Yong YIN*;, Hui-chun YU, Yun-xia YUAN, and Shu-ning XUE
Author Affiliations
  • College of Food and Bioengineering, Henan University of Science and Technology, Luoyang 471023, China
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    DOI: 10.3964/j.issn.1000-0593(2022)07-2290-07 Cite this Article
    Zhi-hao WANG, Yong YIN, Hui-chun YU, Yun-xia YUAN, Shu-ning XUE. Early Warning Method of Apple Spoilage Based on 2D Hyperspectral Information Representation With Pixel Mean and Variance[J]. Spectroscopy and Spectral Analysis, 2022, 42(7): 2290 Copy Citation Text show less
    Hyperspectral image acquisition system
    Fig. 1. Hyperspectral image acquisition system
    Extraction of interest region in apple sample image
    Fig. 2. Extraction of interest region in apple sample image
    The spectral data of apple samples based on the mean representation before and after SG treatment(a): Before SG smoothing; (b): After SG smoothing
    Fig. 3. The spectral data of apple samples based on the mean representation before and after SG treatment
    (a): Before SG smoothing; (b): After SG smoothing
    The spectral data of apple samples based on the variance representation before and after SG treatment(a): Before SG smoothing; (b): After SG smoothing
    Fig. 4. The spectral data of apple samples based on the variance representation before and after SG treatment
    (a): Before SG smoothing; (b): After SG smoothing
    The change of hue angle of apple samples with the storage days
    Fig. 5. The change of hue angle of apple samples with the storage days
    The spectral data of apple samples at 675.11 nm based on mean representation
    Fig. 6. The spectral data of apple samples at 675.11 nm based on mean representation
    The Bhattacharyya distance results of hyperspectral information based on mean representation
    Fig. 7. The Bhattacharyya distance results of hyperspectral information based on mean representation
    Bhattacharyya distance of hyperspectral information based on variance representation
    Fig. 8. Bhattacharyya distance of hyperspectral information based on variance representation
    Bhattacharyya distance results based on 2D hyperspectral information representation with mean and variance
    Fig. 9. Bhattacharyya distance results based on 2D hyperspectral information representation with mean and variance
    贮藏日数/dH*贮藏日数/dH*
    114.431522.23
    215.191621.75
    315.021722.18
    618.682022.88
    716.632124.45
    816.902224.41
    917.342324.18
    1018.682426.17
    1320.302725.99
    1420.85
    Table 1. Hue angle change of apple samples
    贮藏
    日数/d
    失水率贮藏
    日数/d
    失水率贮藏
    日数/d
    失水率
    10.00100.45191.01
    20.04110.52201.11
    30.08120.59211.18
    40.13130.64221.28
    50.17140.70231.36
    60.22150.76241.42
    70.26160.82251.52
    80.31170.88261.61
    90.38180.94271.71
    Table 2. Water loss rate change of apple samples
    表征类型特征波长/nm
    均值428.04635.76652.12676.14689.41705.73960.97
    方差440.09479.52502.58624.50631.15649.05663.88689.92
    Table 3. Feature wavelengths
    Zhi-hao WANG, Yong YIN, Hui-chun YU, Yun-xia YUAN, Shu-ning XUE. Early Warning Method of Apple Spoilage Based on 2D Hyperspectral Information Representation With Pixel Mean and Variance[J]. Spectroscopy and Spectral Analysis, 2022, 42(7): 2290
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