• Spectroscopy and Spectral Analysis
  • Vol. 42, Issue 9, 2924 (2022)
Xiang-jun ZHONG*, Li YANG1; 2; *;, Dong-xing ZHANG1; 2;, Tao CUI1; 2;, Xian-tao HE1; 2;, and Zhao-hui DU1; 2;
Author Affiliations
  • 1. College of Engineering, China Agricultural University, Beijing 100083, China
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    DOI: 10.3964/j.issn.1000-0593(2022)09-2924-07 Cite this Article
    Xiang-jun ZHONG, Li YANG, Dong-xing ZHANG, Tao CUI, Xian-tao HE, Zhao-hui DU. Prediction of Organic Matter Content in Sandy Fluvo-Aquic Soil by Visible-Near Infrared Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2022, 42(9): 2924 Copy Citation Text show less
    Spectral data acquisition device1: PC; 2: Multifunctional testing platform; 3: Standard white plate; 4: Fiber probe; 5: NIR Quest; 6: QE Pro; 7: Soil samples; 8: Light source
    Fig. 1. Spectral data acquisition device
    1: PC; 2: Multifunctional testing platform; 3: Standard white plate; 4: Fiber probe; 5: NIR Quest; 6: QE Pro; 7: Soil samples; 8: Light source
    Outlier filtering results of MCCV method
    Fig. 2. Outlier filtering results of MCCV method
    Spectral reflectance curves
    Fig. 3. Spectral reflectance curves
    Results of different methods of screening characteristic variables(a): Variables selected by CARS method; (b): The distribution of variables selected by CARS; (c): Variables selected by SPA method; (d): The distribution of variables selected by SPA; (e): Variables selected by CARS-SPA method; (f): The distribution of variables selected by CARS-SPA; (g): The distribution of variables selected by UVE; (h): The distribution of variables selected by VCPA
    Fig. 4. Results of different methods of screening characteristic variables
    (a): Variables selected by CARS method; (b): The distribution of variables selected by CARS; (c): Variables selected by SPA method; (d): The distribution of variables selected by SPA; (e): Variables selected by CARS-SPA method; (f): The distribution of variables selected by CARS-SPA; (g): The distribution of variables selected by UVE; (h): The distribution of variables selected by VCPA
    Modeling results of PLSR using different variables(a): All-PLSR; (b): CARS-PLSR; (c): SPA-PLSR; (d)CARS-SPA-PLSR; (e): UVE-PLSR; (f): VCPA-PLSR
    Fig. 5. Modeling results of PLSR using different variables
    (a): All-PLSR; (b): CARS-PLSR; (c): SPA-PLSR; (d)CARS-SPA-PLSR; (e): UVE-PLSR; (f): VCPA-PLSR
    样本数量最大值/(g·kg-1)最小值/(g·kg-1)平均值/(g·kg-1)标准差/(g·kg-1)变异系数/%
    6042.4115.124.987.4029.63
    Table 1. Statistics of SOM content
    Xiang-jun ZHONG, Li YANG, Dong-xing ZHANG, Tao CUI, Xian-tao HE, Zhao-hui DU. Prediction of Organic Matter Content in Sandy Fluvo-Aquic Soil by Visible-Near Infrared Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2022, 42(9): 2924
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