| 光谱学与光谱分析 |
|
|
|
|
|
| Study of Rapid Prediction of Wood Surface Glossiness by Near Infrared Spectroscopy |
| LIU Ya-na, YANG Zhong*, Lü Bin, ZHANG Mao-mao, WANG Xing-hua |
| Research Institute of Wood Industry，Chinese Academy of Forestry, Beijing 100091, China |
|
|
|
|
Abstract Surface glossiness is one of the important visual appearance parameters of natural polymer material (wood) and its related products. To realize the fast measurement of natural polymer material surface glossiness is of great significance to the online quality control and assessment of its surface. In order to broaden the application of near infrared (NIR) spectroscopy in the field of polymer material surface quality control and realize the feasibility of NIR as a fast measurement of surface glossiness , the NIR combined with partial least squares(PLS) analysis were used to analyse the correlations of natural polymer material wood surface glossiness between the NIR predicted and lab measured, and then to investigate the feasibility of NIR to rapidly predict the surface glossiness of natural polymer material wood.The results showed that the wood NIR diffuse reflectance spectroscopy regularly varied with the different wood surface glossiness, from which we can concluded that the NIR spectrums reflected the information of wood surface glossiness. The correlation coefficients of surface glossiness between the PLS models predicted and lab measured were up to 0.90. Additionally, by changing the degree between the fiber and sample surface, we collected the different wood NIR spectrums, the accuracy of NIR surface glossiness models based on these NIR spectrums had not significantly improved, and models based on the NIR spectrums collected by the 90 degree between the fiber and sample surface performed better.
|
|
Received: 2013-05-31
Accepted: 2013-09-07
|
|
|
|
Corresponding Authors:
YANG Zhong
E-mail: zyang@caf.ac.cn
|
|
[1] Farrokhpay S, Morris G E, Fornasiero D, et al. Journal of Coatings Technology and Research, 2006, 3(4): 275.
[2] Faucheu J, Wood K A, Sung L P, et al. Journal of Coatings Technology and Research, 2006, 3(1): 29.
[3] Sousa F J P, Júnior N V, Weingaertner W L, et al. Journal of Materials Science, 2007, 42(24): 10124.
[4] LENG Cai-feng, GUO Ling-hua, LIU Guo-dong, et al(冷彩凤, 郭凌华, 刘国栋，等). Packaging Engineering(包装工程), 2012, 33(11): 112.
[5] Schaller C, Rogez D. Journal of Coatings Technology and Research, 2007, 4(4): 401.
[6] CHEN Xiao-li, PAN Biao(陈潇俐, 潘 彪). China Forestry Science and Technology(林业科技开发), 2006, 20(2): 29.
[7] LIU Yi-xing, LI Jian, GUO Ming-hui, et al(刘一星, 李 坚, 郭明晖, 等). Journal of Northeast Forestry University(东北林业大学学报), 1995, 23(1): 52.
[8] Wu J H, Peng Y K, Li Y Y, et al. Journal of Food Engineering, 2012, 109(2): 267.
[9] Falla F S, Larini C, Le Roux G A C, et al. Journal of Petroleum Science and Engineering, 2006, 51(1-2): 127.
[10] Pfautsch S, Macfarlane C, Ebdon N, et al. Trees, 2012, 26(3): 963.
[11] Inagaki T, Schwanninger M, Kato R, et al. Wood Science and Technology, 2012, 46(1-3): 143.
[12] Jones P D, Schimleck L R, Peter G F, et al. Wood Science and Technology, 2005, 39(7): 529. |
| [1] |
WANG Wen-xiu, PENG Yan-kun*, FANG Xiao-qian, BU Xiao-pu. Characteristic Variables Optimization for TVB-N in Pork Based on Two-Dimensional Correlation Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2094-2100. |
| [2] |
LE Ba Tuan1, 3, XIAO Dong1*, MAO Ya-chun2, SONG Liang2, HE Da-kuo1, LIU Shan-jun2. Coal Classification Based on Visible, Near-Infrared Spectroscopy and CNN-ELM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2107-2112. |
| [3] |
LIU Jin, LUAN Xiao-li*, LIU Fei. Near Infrared Spectroscopic Modelling of Sodium Content in Oil Sands Based on Lasso Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2274-2278. |
| [4] |
YU Hui-ling1, MEN Hong-sheng2, LIANG Hao2, ZHANG Yi-zhuo2*. Near Infrared Spectroscopy Identification Method of Wood Surface Defects Based on SA-PBT-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1724-1728. |
| [5] |
XU Wei-jie1, WU Zhong-chen1, 2*, ZHU Xiang-ping2, ZHANG Jiang1, LING Zong-cheng1, NI Yu-heng1, GUO Kai-chen1. Classification and Discrimination of Martian-Related Minerals Using Spectral Fusion Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1926-1932. |
| [6] |
LI Ying1, LI Yao-xiang1*, LI Wen-bin2, JIANG Li-chun3. Model Optimization of Wood Property and Quality Tracing Based on Wavelet Transform and NIR Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1384-1392. |
| [7] |
DU Jian1, 2, HU Bing-liang1*, LIU Yong-zheng1, WEI Cui-yu1, ZHANG Geng1, TANG Xing-jia1. Study on Quality Identification of Macadamia nut Based on Convolutional Neural Networks and Spectral Features[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1514-1519. |
| [8] |
HAN Guang, LIU Rong*, XU Ke-xin. Extraction of Effective Signal in Non-Invasive Blood Glucose Sensing with Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1599-1604. |
| [9] |
WANG Li-shuang, ZHANG Wen-bo*, TONG Li. Studies on Dimensional Stability of Wood under Different Moisture Conditions by Near Infrared Spectroscopy Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1066-1069. |
| [10] |
HUANG Hua1, WU Xi-yu2, ZHU Shi-ping1*. Feature Wavelength Selection and Efficiency Analysis for Paddy Moisture Content Prediction by Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1070-1075. |
| [11] |
LI Hao-guang1,2, YU Yun-hua1,2, PANG Yan1, SHEN Xue-feng1,2. Study of Maize Haploid Identification Based on Oil Content Detection with Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1089-1094. |
| [12] |
PENG Cheng1, FENG Xu-ping2*, HE Yong2, ZHANG Chu2, ZHAO Yi-ying2, XU Jun-feng1. Discrimination of Transgenic Maize Containing the Cry1Ab/Cry2Aj and G10evo Genes Using Near Infrared Spectroscopy (NIR)[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(04): 1095-1100. |
| [13] |
XIA Ji-an1, YANG Yu-wang1*, CAO Hong-xin2, HAN Chen1, GE Dao-kuo2, ZHANG Wen-yu2. Classification of Broad Bean Pest of Visible-Near Infrared Spectroscopy Based on Cloud Computing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 756-760. |
| [14] |
MAO Ya-chun, WANG Dong, WANG Yue, LIU Shan-jun*. A FeO/TFe Determination Method of BIF Based on the Visible and Near-Infrared Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 765-770. |
| [15] |
LAI Tian-yue1, CAI Feng-huang1*, PENG Xin2*, CHAI Qin-qin1, LI Yu-rong1, 3, WANG Wu1, 3. Identification of Tetrastigma hemsleyanum from Different Places with FT-NIR Combined with Kernel Density Estimation Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(03): 794-799. |
|
|
|
|
