光谱学与光谱分析 |
|
|
|
|
|
Near Infrared Spectroscopy of Wood Sections and Rapid Density Prediction |
JIANG Ze-hui1, HUANG An-min1*,WANG Bin2 |
1. Research Institute of Wood Industry,Chinese Academy of Forestry,Beijing 100091, China 2. Beijing Golden Way Scientific Co., Ltd,Beijing 100070, China |
|
|
Abstract In the present report, the use of calibrated near infrared (NIR) spectroscopy for rapid prediction of solid wood density is described. NIR spectra were obtained from the three sections (cross, radial and tangential section) of each Chinese fir sample. The authors found that the spectra of the three sections are different and the correlation coefficients between the laboratory-determined density and the NIR-fitted data are different, too. The prediction results showed that the model based on NIR spectra taken from the cross section is the best, and the correlation coefficients are 0.94 (cross), 0.85 (radial) and 0.81 (tangential),respectively. Using the cross model to predict the density of unknown wood samples, we can see: r2 is 0.977 and the standard deviation (STDEV) is only 0.006.
|
Received: 2006-01-26
Accepted: 2006-04-25
|
|
Corresponding Authors:
HUANG An-min
|
|
Cite this article: |
JIANG Ze-hui,HUANG An-min,WANG Bin. Near Infrared Spectroscopy of Wood Sections and Rapid Density Prediction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2006, 26(06): 1034-1037.
|
|
|
|
URL: |
https://www.gpxygpfx.com/EN/Y2006/V26/I06/1034 |
[1] CHEN Da,WANG Fang,SHAO Xue-guang,et al(陈 达,王 芳,邵学广,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2004,24(6):672. [2] LU Wan-zhen, YUAN Hong-fu, XU Guang-tong,et al(陆婉珍,袁洪福,徐广通,等). Modern Near Infrared Spectroscopy Analysis Technology(现代近红外分析技术). Beijing:China Petrochemical Press(北京:中国石化出版社), 2001. 73. [3] CHENG Jun-qing(成俊卿). Wood Science(木材学). Beijing:China Forestry Publishing House(北京:中国林业出版社) ,1985. 463. [4] YIN Si-ci(尹私慈). Wood Science(木材学). Beijing:China Forestry Publishing House(北京:中国林业出版社),1996. 22.
|
[1] |
BAI Xi-lin1, 2, PENG Yue1, 2, ZHANG Xue-dong1, 2, GE Jing1, 2*. Ultrafast Dynamics of CdSe/ZnS Quantum Dots and Quantum
Dot-Acceptor Molecular Complexes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 56-61. |
[2] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[3] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[4] |
HAO Zi-yuan1, YANG Wei1*, LI Hao1, YU Hao1, LI Min-zan1, 2. Study on Prediction Models for Leaf Area Index of Multiple Crops Based on Multi-Source Information and Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3862-3870. |
[5] |
WAN Mei, ZHANG Jia-le, FANG Ji-yuan, LIU Jian-jun, HONG Zhi, DU Yong*. Terahertz Spectroscopy and DFT Calculations of Isonicotinamide-Glutaric Acid-Pyrazinamide Ternary Cocrystal[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3781-3787. |
[6] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[7] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[8] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[9] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[10] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[11] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[12] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[13] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[14] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[15] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
|
|
|
|