| 光谱学与光谱分析 |
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| Proximate Analysis of Straw by Near Infrared Spectroscopy(NIRS) |
| HUANG Cai-jin, HAN Lu-jia*,LIU Xian, YANG Zeng-ling |
| College of Engineering，China Agricultural University，Beijing 100083，China |
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Abstract Proximate analysis is one of the routine analysis procedures in utilization of straw for biomass energy use. The present paper studied the applicability of rapid proximate analysis of straw by near infrared spectroscopy (NIRS) technology, in which the authors constructed the first NIRS models to predict volatile matter and fixed carbon contents of straw. NIRS models were developed using Foss 6500 spectrometer with spectra in the range of 1 108-2 492 nm to predict the contents of moisture, ash, volatile matter and fixed carbon in the directly cut straw samples; to predict ash, volatile matter and fixed carbon in the dried milled straw samples. For the models based on directly cut straw samples, the determination coefficient of independent validation (R2V) and standard error of prediction (SEP) were 0.92% and 0.76% for moisture, 0.94% and 0.84% for ash, 0.88% and 0.82% for volatile matter, and 0.75% and 0.65% for fixed carbon, respectively. For the models based on dried milled straw samples, the determination coefficient of independent validation (R2V) and standard error of prediction (SEP) were 0.98% and 0.54% for ash, 0.95% and 0.57% for volatile matter, and 0.78% and 0.61% for fixed carbon, respectively. It was concluded that NIRS models can predict accurately as an alternative analysis method, therefore rapid and simultaneous analysis of multi-components can be achieved by NIRS technology, decreasing the cost of proximate analysis for straw.
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Received: 2008-02-08
Accepted: 2008-05-12
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Corresponding Authors:
HAN Lu-jia
E-mail: hanlj@cau.edu.cn
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[1] HAN Lu-jia, YAN Qiao-juan, LIU Xiang-yang, et al(韩鲁佳，闫巧娟，刘向阳，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2002，18(3)：87.
[2] LIU Rong-hou, NIU Wei-sheng, ZHANG Da-lei(刘荣厚，牛卫生，张大雷). Thermo-Chemical Conversion Technologies for Biomass Resources(生物质热化学转换技术). Beijing: Chemical Industry Press(北京：化学工业出版社)，2005. 16.
[3] XIE Li-juan, YING Yi-bin, YU Hai-yan, et al(谢丽娟, 应义斌, 于海燕, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(6): 1131.
[4] LIU Xian, HAN Lu-jia(刘 贤，韩鲁佳). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(11): 2016.
[5] Luypaert J, Massart D L, Vander Heyden Y. Talanta, 2007, 72: 865.
[6] Chung H. Applied Spectroscopy Reviews, 2007, 42(3): 251.
[7] Cozzolino D, Fassio A, Fernández E, et al. Animal Feed Science and Technology, 2006, 129: 329.
[8] Kaihara M, Takahashi R, Akazawa T, et al. Spectroscopy Leters, 2002, 35: 369.
[9] Andres J M, Bona M T. Analytical Chimica Acta, 2005, 535: 123.
[10] LI Feng-rui, XIAO Bao-lan, TANG Yu-guo, et al(李凤瑞，肖宝兰，唐玉国，等). Power System Engineering(电站系统工程)，2003，19(6)：19.
[11] LI Feng-rui, TANG Yu-guo, XIAO Bao-lan(李凤瑞，唐玉国，肖宝兰). Journal of Engineering for Thermal Energy and Power(热能动力工程)，2003，18: 582.
[12] WU Bei-lei, LIN Zhen-xing, WANG Qun-wei, et al(邬蓓蕾，林振兴，王群威，等). Rock and Mineral Analysis(岩矿测试)，2006，25(2): 133.
[13] Jensen P D, Hartmann H, Bohm T, et al. Biomass and Bioenergy, 2006, 30: 935.
[14] LIU Li-ying, CHEN Hong-zhang(刘丽英，陈洪章). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(2): 275.
[15] Jin S, Chen H. Ind. Crops Prod. (2007),doi:10.1016/j.indcrop.2007.03.004
[16] CEN/TS 14774-1:2004. Solid Biofuels-Methods for Determination of Moisture Content-Oven Dry Method-Part 1: Total Moisture Reference Method.
[17] CEN/TS 14775:2004. Solid Biofuels-Methods for the Determination of ash Ｃontent.
[18] CEN/TS 15148:2005. Solid Biofuels-Methods for the Determination of the Content of Volatile Matter.
[19] Cozzolino D, Moronb A. Animal Feed Science and Technology, 2004, 111: 161.
[20] Malley D F, Mcclure C, Martin P D. Communications in Soil Science & Plant Analysis, 2005, 36: 455.
[21] Jenkins B M, Baxter L L, Miles Jr T R, et al. Fuel Processing Technology, 1998, 54: 17.
[22] Nielsen C, Westborg S. Analysis of Straw and Straw Ashes[R]. Part 1-4. DK-TEKNIK. 1994.
[23] Burns D A, Ciurczak E W. Handbook of Near-Infrared Analysis, Second Edition, Revised and Expanded. New York: Marcel Dekker，2001. 431.
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