• Spectroscopy and Spectral Analysis
  • Vol. 42, Issue 10, 3256 (2022)
Bing WU, Ke-ming YANG*;, Wei GAO, Yan-ru LI, Qian-qian HAN, and Jian-hong ZHANG
Author Affiliations
  • College of Geoscience and Surveying Engineering, China University of Mining & Technology (Beijing), Beijing 100083, China
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    DOI: 10.3964/j.issn.1000-0593(2022)10-3256-07 Cite this Article
    Bing WU, Ke-ming YANG, Wei GAO, Yan-ru LI, Qian-qian HAN, Jian-hong ZHANG. EC-PB Rules for Spectral Discrimination of Copper and Lead Pollution Elements in Corn Leaves[J]. Spectroscopy and Spectral Analysis, 2022, 42(10): 3256 Copy Citation Text show less
    Original spectral data of corn leaves under different stress gradients(a): Original spectral curve of corn leaves under different Cu2+ concentration stress; (b): Original spectral curve of corn leaves under different Pb2+ concentration stress
    Fig. 1. Original spectral data of corn leaves under different stress gradients
    (a): Original spectral curve of corn leaves under different Cu2+ concentration stress; (b): Original spectral curve of corn leaves under different Pb2+ concentration stress
    Spectra processed by different spectral conversions
    Fig. 2. Spectra processed by different spectral conversions
    Correlation between CLI and Cu-Pb element types of each order
    Fig. 3. Correlation between CLI and Cu-Pb element types of each order
    The distribution of two-dimensional copper-lead discriminant feature points and discriminant rule lines in the training set
    Fig. 4. The distribution of two-dimensional copper-lead discriminant feature points and discriminant rule lines in the training set
    The distribution of two-dimensional copper-lead discriminant feature points and discriminant rule lines in the verification set
    Fig. 5. The distribution of two-dimensional copper-lead discriminant feature points and discriminant rule lines in the verification set
    The distribution of three-dimensional copper-lead discriminant feature points and discriminant rule plans in the training set
    Fig. 6. The distribution of three-dimensional copper-lead discriminant feature points and discriminant rule plans in the training set
    The distribution of three-dimensional copper-lead discriminant feature points and discriminant rule plans in the verification set
    Fig. 7. The distribution of three-dimensional copper-lead discriminant feature points and discriminant rule plans in the verification set
    土壤胁迫梯度/
    (μg·g-1)
    叶片Cu2+含量/
    (μg·g-1)
    叶片Pb2+含量/
    (μg·g-1)
    505.922.28
    1006.563.60
    1508.305.70
    2009.260.28
    3008.760.46
    4009.1911.18
    60012.8017.36
    80014.1732.16
    1 00016.5933.24
    Table 1. Cu2+ and Pb2+ contents in corn leaves under different concentration gradients
    FOD阶次相关系数绝对值最大值相关系数绝对值平均值
    0.10.399 70.193 5
    0.20.425 10.194 0
    0.30.441 90.193 0
    0.40.462 80.190 0
    0.50.458 60.184 5
    0.60.406 10.177 0
    0.70.411 10.169 3
    0.80.450 30.160 9
    0.90.481 70.146 3
    1.00.492 50.133 5
    1.10.476 40.128 0
    1.20.474 20.123 7
    1.30.473 20.120 7
    1.40.470 50.118 3
    1.50.466 40.116 6
    1.60.460 70.115 5
    1.70.453 40.114 8
    1.80.453 90.114 4
    1.90.453 80.114 0
    2.00.448 00.113 7
    Table 2. Correlation coefficients between FOD of each order and heavy metal copper-lead element types
    Bing WU, Ke-ming YANG, Wei GAO, Yan-ru LI, Qian-qian HAN, Jian-hong ZHANG. EC-PB Rules for Spectral Discrimination of Copper and Lead Pollution Elements in Corn Leaves[J]. Spectroscopy and Spectral Analysis, 2022, 42(10): 3256
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