光谱学与光谱分析 |
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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 |
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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.
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Received: 2006-03-28
Accepted: 2006-06-28
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Corresponding Authors:
YANG Zhong
E-mail: zyang@caf.ac.cn
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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.
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URL: |
https://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.
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