Yi-jia LUO, He ZHU, Xiao-han LI, Juan DONG, Hao TIAN, Xue-wei SHI, Wen-xia WANG, Jing-tao SUN. Quantitative Analysis of Total Phenol Content in Cabernet Sauvignon Grape Based on Near-Infrared Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2021, 41(7): 2036
- Spectroscopy and Spectral Analysis
- Vol. 41, Issue 7, 2036 (2021)
Fig. 1. Raw spectra of Cabernet Sauvignon in different harvest stages
Fig. 2. Mean raw spectra of Cabernet Sauvignon in different harvest stages
Fig. 3. Reflectance spectra obtained after MSC
Fig. 4. Characteristic wavebands selected by CARS algorithm
(a): Variation of the number of selected wavelength variables; (b): Variation of RMSECV;(c): The changing trend of variable regression coefficients
(a): Variation of the number of selected wavelength variables; (b): Variation of RMSECV;(c): The changing trend of variable regression coefficients
Fig. 5. (a) Frequency distribution of bands selected by GA; (b) Selection of wavelengths by the GA method
Fig. 6. Characteristic wavebands selected by si-PLS method
Fig. 7. Plot of 50 wavelengths selected by SPA
Fig. 8. The influence of the number of neurons in different hidden layers on the performance of the model
Fig. 9. Scatter plot of real value and predicted value of total phenol content
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Table 1. Statistical results of total phenol content of Cabernet Sauvignon in different harvest stages (mg·g-1)
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Table 2. Results of ELM modeling for the total phenol content with different pre-treatment methods
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Table 3. Performance comparison of different activation functions
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Table 4. The prediction results of ELM and GA-ELM models
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