| 光谱学与光谱分析 |
|
|
|
|
|
| Effects of Spectral Pretreatment on the Prediction of Crystallinity of Wood Cellulose Using Near Infrared Spectroscopy |
| JIANG Ze-hui, FEI Ben-hua, YANG Zhong* |
| Chinese Academy of Forestry，Beijing 100091，China |
|
|
|
|
Abstract The crystallinity of wood has an important effect on the physical, mechanical and chemical properties of cellulose fibers. The aims of this study were to investigate the ability of near infrared spectroscopy (NIR) to predict the crystallinity of wood cellulose and the effect of spectral pretreatment on the prediction of crystallinity in wood cellulose using near infrared spectroscopy (NIR). Near infrared diffuse reflectance spectra were collected from wood powder with a fiber-optical probe and the crystallinity of wood was determined by X-ray diffractometer (XRD) in this experiment. The results showed that near infrared spectroscopy coupled with partial least square (PLS) regression could be correlated with the crystallinity of plantation wood, and the ability of NIR prediction based on original spectra was better than that based on the first derivative or second derivative treated spectra. There was a significant correlation between NIR spectra and XRD determined crystallinity with a correlation coefficient of 0.950 and a low RMSEP. Near infrared spectroscopy coupled with multivariate data anlaysis has proven to be an accurate and fast method for rapid prediction of wood crystallinity.
|
|
Received: 2006-03-28
Accepted: 2006-06-28
|
|
|
|
Corresponding Authors:
YANG Zhong
E-mail: zyang@caf.ac.cn
|
|
| Cite this article: |
|
JIANG Ze-hui,FEI Ben-hua,YANG Zhong. Effects of Spectral Pretreatment on the Prediction of Crystallinity of Wood Cellulose Using Near Infrared Spectroscopy [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2007, 27(03): 435-438.
|
|
|
|
| URL: |
|
http://www.gpxygpfx.com/EN/Y2007/V27/I03/435 |
[1] Andersson S, Serimaa R, Paakkari T, et al. Journal of Wood Science, 2003, 49(6): 531.
[2] Lee C L. Forest Products Journal, 1961, 11: 108.
[3] Ahtee M, Hattula T, Mangs J, et al. Paperi Ja Puu(Paper and Timber), 1988, (8): 475.
[4] Newman R H, Hemmingson J A. Holzforschung, 1990, 44: 351.
[5] Wolcott M P, Yin S, Rials T G. Composite Interface, 2000, 7(1): 3.
[6] Tsuchikawa S, Inoue K, Noma J. Journal of Wood Science, 2003, 49(1): 29.
[7] Wright J A, Birkett M D, Gambino M J T. Technical Association of the Pulp and Paper Industry(TAPPI) Journal, 1990, 73(8): 164.
[8] Kelley S S, Rials T G, Snell R, et al. Wood Science and Technology, 2004, 38(4): 257.
[9] Schimleck L R, Michell A J, Raymond C A, et al. Canadian Journal of Forest Research, 1999, 29(2): 194.
[10] Schimleck L R, Evans R. International Association of Wood Anatomists(IAWA) Journal, 2002, 3(3): 217.
[11] Thumm A, Meder R. Journal of Near Infrared Spectroscopy, 2001, 9(2): 117.
[12] Kelley S S, Rials T G, Groom L R, et al. Holzforschung, 2004, 58(3): 252.
[13] Schimleck L R, Evans R. International Association of Wood Anatomists(IAWA) Journal, 2002, 23(3): 225.
[14] Evans R, Ilic J. Forest Products Journal, 2001, 51(3): 53.
[15] So C L, Via B K ,Groom L H, et al. Forest Products Journal, 2004, 54(3): 6.
[16] YANG Zhong, JIANG Ze-hui, FEI Ben-hua, et al(杨 忠, 江泽慧, 费本华, 等). Scientia Silvae Sinicae(林业科学), 2005, 41(4): 177.
[17] JIANG Ze-hui, HUANG An-min, FEI Ben-hua, et al(江泽慧, 黄安民, 费本华, 等). Spectroscopy and Spectral Analysis (光谱学与光谱分析)，2006, 26(7): 1230.
[18] Segal L, Creely J J, Martin Jr, et al. Textile Research Journal, 1959, 29: 786.
[19] Schultz T P, Burns D A. Technical Association of the Pulp and Paper Industry Journal, 1990, 73: 209.
[20] Bokobza L. Journal of Near Infrared Spectroscopy, 1998, 6(1): 3.
[21] Dardenne P, Sinnaeve G, Baeten V. Journal of Near Infrared Spectroscopy, 2000, 8(4):229.
[22] 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(现代近红外光谱分析技术). Beijing：China Petrochemical Press (北京：中国石化出版社), 2001. 165.
[23] GAO Rong-qiang, FAN Shi-fu, YAN Yan-lu, et al(高荣强, 范世福, 严衍禄, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2004, 24(12): 1563.
|
| [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. |
|
|
|
|
