| 光谱学与光谱分析 |
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| Nondestructive Identification of Different Oil Content Maize Kernels by Near-Infrared Spectra |
| ZHANG Yuan1,ZHANG Lu-da1*,BAI Qi-lin2,CHEN Shao-jiang3 |
1. College of Science, China Agricultural University, Beijing 100193, China
2. Crop Genetics Research Institute, Shanxi Academy of Agricultural Sciences, Taiyuan 030031, China
3. National Maize Improvement Center of China, China Agricultural University, Beijing 100193, China |
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Abstract Using 220 maize single kernels, containing 75 common maize single kernels, 72 high-oil maize single kernels and 73 super high-oil maize single kernels as study materials, BPANN identification model was set up for maize single kernel with different oil content based on principal components of near infrared (NIR) spectra. Four fifths of the samples were randomly selected as training set and the other samples as prediction set. Fourteen principal components from the second to the fifteenth were selected as nets input and -1, 0, 1 as nets output. Ten models were set up like this and the accurate identification rate of all the training sets can reach 100%. For prediction sets, fifteen common corn grain samples had an average accurate identification rate of 99.33%, fourteen high-oil corn grain samples had an average accurate identification rate of 97.88%, fourteen super high-oil corn grain samples had an average accurate identification rate of 91.43%, and total maize grains in prediction set had an average accurate identification rate of over 95%. Results showed that NIR spectroscopy combined with BP-ANN technology could identify maize kernels fast and nondestructively according to oil content，which offered a very useful classification method for maize seed breeding. The effect of different principal component on BPANN models was also studied. Results told us that the first principal component with over 99% of variance contribution had negative effect on the identification model. The predictive ability of identification models set up by different principal component was discriminatory, although the learning accurate identification rates were all 100%. So it is necessary to choose correlative principal component to set up identification model.
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Received: 2007-11-02
Accepted: 2008-02-08
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Corresponding Authors:
ZHANG Lu-da
E-mail: zhangld@cau.edu.cn
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[1] SONG Tong-ming(宋同明). Journal of Maize Sciences(玉米科学), 1996, ４(4): 6.
[2] HUO Shi-ping, YAN Qing-jiu(霍仕平, 晏庆九). Journal of Maize Sciences(玉米科学)，1994，2(3)：75.
[3] SONG Tong-ming(宋同明). High-Oil Maize(高油玉米). Beijing: Beijing Agricultural University Press(北京：北京农业大学出版社)，1992.
[4] CHANG Ying, CHEN Shao-jiang, MENG Qing-xiang(常 影，陈绍江，孟庆翔). Journal of China Agricultural University(中国农业大学学报), 2006, 11(2): 54.
[5] Benitez J A, Gernat A G, Murillo J G, et al. Poultry Science, 1999, 78(6): 861.
[6] O’Quinn P R, Nelssen J L, Goodband R D, et al. Journal of Animal Science, 2000, 78(8): 2144.
[7] WANG Wei-dong, WANG Pu(王伟东, 王 璞). Heilongjiang Agricultural Science(黑龙江农业科学), 2002, (2): 41.
[8] SONG Tong-ming(宋同明). Acta Agronomica Sinica(作物学报), 1989, 15(3): 160.
[9] WANG Hai-lian, WAN Xiang-yuan, HU Pei-song, et al(王海莲, 万向元, 胡培松, 等). Scientia Agricultura Sinica(中国农业科学), 2005, 38(8): 1540.
[10] ZHANG Ye-hui, ZHAO Long-lian, LI Xiao-wei, et al(张晔晖, 赵龙莲, 李晓薇, 等). Acta Laser Biology Sinica(激光生物学报), 1998, 7(2): 138.
[11] ZHANG Jun, ZHANG Yi-rong, LU Bao-hong, et al(张 俊, 张义荣, 卢宝红, 等). Journal of Maize Sciences(玉米科学), 2007, 15(3): 62.
[12] DUAN Min-xiao, XING Jin-feng, GUO Jing-lun, et al(段民孝, 邢锦丰, 郭景伦, 等). Beijing Agricultural Sciences(北京农业科学), 2002, 20(1): 11.
[13] YAN Yan-lu, ZHAO Long-lian, LI Jun-hui, et al(严衍禄, 赵龙莲, 李军会, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2000, 20(6): 777.
[14] WEI Liang-ming, YAN Yan-lu, DAI Jing-rui(魏良明, 严衍禄, 戴景瑞). Scientia Agricultura Sinica(中国农业科学), 2004, 37(5): 630.
[15] Hartwig R A, Hurburgh C R. Jr. Journal of the American Oil Chemists' Society, 1990, 67(7): 435.
[16] LI Jun-xia, ZHANG Hong-liang, YAN Yan-lu, et al(李君霞, 张洪亮, 严衍禄, 等). Scientia Agricultura Sinica(中国农业科学), 2006, 39(4): 836.
[17] YU Ru-qin(俞汝勤). Introduction to Chemometrics(化学计量学导论). Changsha: Hunan Education Publishing House(长沙: 湖南教育出版社), 1991. 5.
[18] REN Rui-xue, TANG Zhen, LIU Fu-qiang, et al(任瑞雪, 汤 真, 刘福强, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2001, 21(4): 521.
[19] BAO Yi-dan, WU Yan-ping, HE Yong(鲍一丹, 吴燕平, 何 勇). Journal of Agricultural Mechanization Research(农机化研究)，2004，(3)：162.
[20] QI Xiao-ming, ZHANG Lu-da, DU Xiao-lin, et al(齐小明, 张录达, 杜晓林, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2003, 23(5): 870.
[21] RUI Yu-kui, LUO Yu-bo, HUANG Kun-lun, et al(芮玉奎，罗云波，黄昆仑，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2005, 25(10): 1581.
[22] LI Guo-hui, ZHANG Lu-da, YANG Jian-wen, et al(李国辉，张录达，杨建文，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(10): 1959.
[23] HE Yong, LI Xiao-li, SHAO Yong-ni(何 勇，李晓丽，邵咏妮). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2006, 26(5): 850.
[24] LI Xiao-li, HE Yong, QIU Zheng-jun(李晓丽，何 勇，裘正军). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(2): 279.
[25] CEN Hai-yan, BAO Yi-dan, HE Yong(岑海燕，鲍一丹，何 勇). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(3): 503.
[26] TANG Chang-bo, CHEN Min-yan, PENG Yu-kui，et al(唐长波，陈敏艳，彭玉魁，等). Academic Periodical of Farm Products Processing(农产品加工·学刊), 2007, (3): 52.
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