Zhi-rong YU, Ming-jian HONG. Near-Infrared Spectral Quantitative Analysis Network Based on Grouped Fully Connection[J]. Spectroscopy and Spectral Analysis, 2022, 42(6): 1735
- Spectroscopy and Spectral Analysis
- Vol. 42, Issue 6, 1735 (2022)
Fig. 1. The structure of GFCN model
Fig. 2. The spectra after preprocess
(a): Tecator dataset; (b): IDRC2018 dataset
(a): Tecator dataset; (b): IDRC2018 dataset
Fig. 3. Training loss and test loss of ANN model
(a): FCN; (b): GFCN
(a): FCN; (b): GFCN
Fig. 4. Influences of the number of groups on the predicting effect of GFCN model
Fig. 5. Prediction results of three models
(a): Fat; (b): Moisture; (c): Protein; (d): IDRC2018
(a): Fat; (b): Moisture; (c): Protein; (d): IDRC2018
Fig. 6. The influence of the size of training set on the predictings effect of three models
Fig. 7. Contribution ratio of each wavelength of FCN and GFCN models for predicting fat component
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Table 1. Number of parameters for FCN and GFCN
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Table 2. Comparison of predictings effects of three models
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