Li YUAN, Bin SHI, Jian-cheng YU, Tian-yu TANG, Yuan YUAN, Yan-lin TANG. Application of Different Smoothing Ensemble CARS Algorithm in Spectral Discrimination of Black Tea Grade[J]. Spectroscopy and Spectral Analysis, 2020, 40(10): 3254
- Spectroscopy and Spectral Analysis
- Vol. 40, Issue 10, 3254 (2020)
Fig. 1. Visible-near infrared spectra of black tea
Fig. 2. Characteristic variables selected by MA-ECARS based on different window widths
(a): Window width=3; (b): Window width=17; (c): Window width=31
(a): Window width=3; (b): Window width=17; (c): Window width=31
Fig. 3. Characteristic variables selected by MF-ECARS based on different window widths
(a): Window width=5; (b): Window width=15; (c): Window width=23
(a): Window width=5; (b): Window width=15; (c): Window width=23
Fig. 4. Characteristic variables selected by GF-ECARS based on different window widths
(a): Window width=5; (b): Window width=19; (c): Window width=31
(a): Window width=5; (b): Window width=19; (c): Window width=31
Fig. 5. Characteristic wavelengths selected by SPA
Fig. 6. Characteristic wavelengths selected by CARS
Fig. 7. The prediction results of GF-ECARS-PLSR
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Table 1. The PLSR model result of different pretreatments
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Table 2. Characteristic bands selected by moving window partial least squares (MWPLS)
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Table 3. The PLSR model of different selection methods of characteristic variables
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