Xiang-cheng KAN, Guang-qiang XIE, Yao-xiang LI, Li-hai WANG, Yi-na LI, Jun-ming XIE, Xu TANG. Influence of Temperature Change on the Prediction of Wood Moisture Content by NIR[J]. Spectroscopy and Spectral Analysis, 2022, 42(11): 3387
- Spectroscopy and Spectral Analysis
- Vol. 42, Issue 11, 3387 (2022)
Fig. 1. Wood samples
Fig. 2. Average NIR spectra of wood at different temperatures
(a): Pinus sylvestris; (b): Fraxinus mandshurica; (c): Populus sylvestris; (d): Korean pine
(a): Pinus sylvestris; (b): Fraxinus mandshurica; (c): Populus sylvestris; (d): Korean pine
Fig. 3. The first-order derivative graphs of NIR spectral peaks of wood samples at 1 450 nm
(a): Pinus sylvestris; (b): Fraxinus mandshurica; (c): Populus sylvestris; (d): Korean pine
(a): Pinus sylvestris; (b): Fraxinus mandshurica; (c): Populus sylvestris; (d): Korean pine
Fig. 4. NIR spectral peak shifts of wood samples at different temperatures
(a): Pinus sylvestris; (b): Fraxinus mandshurica; (c): Populus sylvestris; (d): Korean pine
(a): Pinus sylvestris; (b): Fraxinus mandshurica; (c): Populus sylvestris; (d): Korean pine
Fig. 5. Scores of the first two PCs of Pinus sylvestris samples at different temperatures
Fig. 6. Principal component analysis cumulative variance plot
Fig. 7. Result of partial least squares discriminant analysis of different temperatures
Fig. 8. NIR spectra of Pinus sylvestris at different temperature (-20~30℃)
(a): Raw spectra; (b): S-G smoothing+MSC;(c): S-G smoothing+MSC+1st derivative
(a): Raw spectra; (b): S-G smoothing+MSC;(c): S-G smoothing+MSC+1st derivative
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Table 1. Nature of the samples
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Table 2. Experimental equipments
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Table 3. The RMSEP of the validation sets predicted by the calibration models developed by the NIR spectra collected under different temperatures
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Table 4. Prediction results of PLS models using different sectral preprocessing methods
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