Spectroscopy and Spectral Analysis, Volume. 42, Issue 11, 3387(2022)
Influence of Temperature Change on the Prediction of Wood Moisture Content by NIR
Figures & Tables(12)
Fig. 2. Average NIR spectra of wood at different temperatures
(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
Fig. 4. NIR spectral peak shifts of wood samples at different temperatures
(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. 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
Table 1. Nature of the samples
View tableView in ArticleTable 1. Nature of the samples
树种 胸径
/cm距髓心
距离/cm最高含水
率/%最低含水
率/%平均含水
率/%樟子松 24 6 35.35 65.92 56.62 水曲柳 20 6 30.95 54.36 43.16 大青杨 22 6 39.36 75.23 68.6 红松 17 6 34.26 63.84 51.23
Table 2. Experimental equipments
View tableView in ArticleTable 2. Experimental equipments
设备名称 型号 参数 生产厂家 LabSpec便携式快速扫描光谱仪 FR /A114260 波长范围为: 350~2 500 nm 美国ASD公司 红外线测温仪 TA601 测量精度: ±1 ℃; 发射率ε: 0.1~1.0 中国特安斯公司 烘箱 401-3BC 温度范围: 50~300 ℃; 均匀度: ±1% 杭州蓝天化验仪器厂 恒温恒湿箱 CLC-B2V-M 温度范围0~99 ℃; 温度波动±0.1 ℃;
湿度范围10~95%rH德国MMM group公司 冰柜 BD/C-100A 最低温度-30 ℃ 中国容声公司
Table 3. The RMSEP of the validation sets predicted by the calibration models developed by the NIR spectra collected under different temperatures
View tableView in ArticleTable 3. The RMSEP of the validation sets predicted by the calibration models developed by the NIR spectra collected under different temperatures
Table 4. Prediction results of PLS models using different sectral preprocessing methods
View tableView in ArticleTable 4. Prediction results of PLS models using different sectral preprocessing methods
校正集 验测集 Rc RMSEC Rp RMSEP 无预处理 樟子松 0.837 0.130 0.826 0.156 水曲柳 0.850 0.125 0.832 0.151 大青杨 0.786 0.186 0.776 0.192 红松 0.845 0.133 0.812 0.169 MA 樟子松 0.867 0.111 0.861 0.132 水曲柳 0.869 0.109 0.866 0.121 大青杨 0.821 0.145 0.801 0.152 红松 0.865 0.123 0.860 0.135 SG 樟子松 0.897 0.121 0.890 0.129 水曲柳 0.889 0.103 0.881 0.115 大青杨 0.886 0.123 0.879 0.123 红松 0.911 0.113 0.905 0.123 MSC 樟子松 0.907 0.101 0.900 0.114 水曲柳 0.882 0.101 0.881 0.114 大青杨 0.886 0.112 0.881 0.106 红松 0.911 0.103 0.911 0.120 一次微分 樟子松 0.948 0.096 0.942 0.099 水曲柳 0.950 0.095 0.948 0.095 大青杨 0.949 0.099 0.945 0.102 红松 0.959 0.092 0.940 0.113 SG+MSC 樟子松 0.943 0.099 0.939 0.102 水曲柳 0.956 0.088 0.948 0.094 大青杨 0.940 0.102 0.935 0.109 红松 0.951 0.082 0.947 0.088 SG+MSC+
一次微分樟子松 0.978 0.086 0.972 0.085 水曲柳 0.981 0.075 0.978 0.074 大青杨 0.979 0.076 0.975 0.080 红松 0.979 0.082 0.977 0.088
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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
Paper Information
Category: Research Articles
Received: Sep. 8, 2021
Accepted: --
Published Online: Nov. 23, 2022
The Author Email: KAN Xiang-cheng (kxc@nefu.edu.cn)
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