光谱学与光谱分析 |
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Determination of Translocatable Matter of Rice by Near Infrared Reflectance Spectroscopy |
XIN Yang, YANG Zhong-fa*, LUO Hai-wei, YAO Li-ming, ZHU Peng |
College of Agronomy, Hainan University, Haikou 570228, China |
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Abstract The potential of predicting translocatable matter of rice with near infrared reflectance spectroscopy (NIRS) was studied. Using 7 varieties of rice planted in Danzhou of Hainan province as materials, the method of neutral detergent fiber added amylase with NIRS was examined to establish calibration model of predicting translocatable matter of stem and panicle of rice. The results indicated that partial least square(PLS1) is the best regression statistic method for calibration model; The differences of results of the spectral data pretreatment methods for calibration model were insignificant; Because of the high prediction accuracy, the final calibration model was chosen using “no spectral data pretreatment”+“PLS1”; Determination coefficient of external validation and root mean square errors of prediction of the calibration model of stem and panicle was 0.991 2, 0.008 1, 0.961 1 and 0.022 6, respectively.
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Received: 2012-03-05
Accepted: 2012-06-10
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Corresponding Authors:
YANG Zhong-fa
E-mail: y-hn@163.com
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[1] Thornley J H M. Ann. Bot., 1977, 41: 1191. [2] YANG Zhongfa, Inoue Natot, Fujila Kaori, et al. Jpn. J. Crop. Sci., 2004, 73: 416. [3] Akira A B E. Memoirs of National Institute of Animal Industry, 1988, 2: 16. [4] Ohnishi Masao, Horie Takeshi. Jpn. J. Crop. Sci., 1999, 68: 126. [5] Yamaguchi Yasuhiro, Tsukaguchi Tadashi, Inoue Ken-ichi, et al. The Hokuriku Crop Science, 2006, 41: 35. [6] LIN Xian-qing, WANG Ya-fen, ZHU De-feng, et al(林贤青,王雅芬,朱德峰,等). Chinese Journal of Rice Science(中国水稻科学), 2001, 15(2): 155. [7] Koga Teruaki, Akira A B E. Japanese Society of Grassland Science, 1994, 40: 8. [8] HAO Xiao-yan, ZHANG Ju-song, GU Wei-hong, et al(郝小燕,张巨松,顾卫红,等). Journal of the Chinese Cereals and Oils Association(中国粮油学报), 2009, 24(4): 168. [9] LUO Qian-gu, WU Jun-biao, ZHOU Jiu-yao, et al(罗千古,吴俊标,周玖瑶,等). China Medical Herald(中国医药导报), 2011, 8(3): 56. [10] ZHANG Wan-ming, WANG Zhi-ming, CHEN Kai-lu, et al(张万明,王志明,陈开陆,等). Chinese Journal of Spectroscopy Laboratory(光谱实验室), 2010, 27(2): 435. [11] WANG Hong-jun, DENG Xu-ming, JIANG Hong, et al(王宏军,邓旭明,蒋 红,等). China Feed(中国饲料), 2011,(4): 39. [12] SUI Lian-min, ZHANG Li-ying, LI De-fa, et al(隋连敏,张丽英,李德发,等). Feed Industry(饲料工业), 2002, 23(10): 26. [13] MA Dong-hong, WANG Xi-chang, LIU Li-ping, et al(马冬红,王锡昌,刘利平,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2011, 31(4): 877. [14] LI Shui-fang, SHAN Yang, ZHU Xiang-rong, et al(李水芳,单 杨,朱向荣,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2011, 27(8): 350. [15] CAO Wei-xing(曹卫星). Crop Cultivation Science(作物栽培学总论). Beijing: Science Press(北京:科学出版社), 2006. 132. [16] XUE Hong-feng, MENG Qing-xiang(薛红枫,孟庆翔). Chinese Journal of Animal Science(中国畜牧杂志), 2006, 42(19): 41. [17] WU Jian-guo, SHI Chun-hai, ZHANG Xiao-ming, et al(吴建国,石春海,张小明,等). Acta Agronomica Sinica(作物学报), 2003, 29(5): 688. [18] SHU Qing-yao, WU Dian-xing, XIA Ying-wu, et al(舒庆尧,吴殿星,夏英武,等). Chinese Journal of Rice Science(中国水稻科学), 1999, 13(3): 189. [19] ZHU Rong-guang, HAN Lu-jia, YANG Zeng-ling, et al(朱荣光,韩鲁佳,杨增玲,等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2006, 25(4): 267. [20] LU Wan-zhen, YUAN Hong-fu, XU Guang-tong, et al(陆婉珍,袁洪福,徐广通,等). Modern Near Infrared Spectrum Analysis Technique(现代近红外光谱分析技术). Beijing: China Petrochemical Press(北京:中国石化出版社), 2000. 10.
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