光谱学与光谱分析 |
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The Utility Research on NIR Diffuse Reflectance and Transmittance Measurements Mode in Authenticity Identification of Maize Population Samples |
TANG Xing-tian1, WU Wen-jin1, GUO Ting-ting2, JIA Shi-qiang1, YAN Yan-lu1, AN Dong1* |
1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China 2. National Maize Improvement Center of China, China Agricultural University, Beijing 100193, China |
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Abstract In the present paper, the spectral measurements of maize population samples were researched so as to identify their authenticity. Diffuse reflectance and transmittance measure modes were used to collect spectral data of 8 maize varieties. DPLS-DA was used to compress pretreated data. The recognition models of eight maize varieties were built based on biomimetic pattern recognition (BPR). The average correct recognition rate and average correct rejection rate of identification models built by two modes were calculated. The average recognition rate and rejection rate of diffuse reflectance method reached 94.50% and 96.71%, and those of transmittance method reached 98.5% and 98.00%, respectively. Both of them met the requirements of maize preliminary screening., and the recognition rate and rejection rate of transmittance method are higher than diffuse reflectance method by 4% and 1.3% respectively.
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Received: 2011-06-05
Accepted: 2011-11-20
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Corresponding Authors:
AN Dong
E-mail: andong@semi.ac.cn;anclear@gmail.com
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[1] WANG Guo-sheng, CHEN Ju-lin, HOU Wei, et al(王国胜,陈举林,侯 玮,等). Anhui Agricultural Science Bulletin(安徽农学通报), 2010, 16(23): 77. [2] WEN Xiao-rong, LU Jing(温晓荣,芦 静). Morden Seed(现代种业), 2009, 3: 42. [3] ZHAO Jiu-ran, WANG Rong-huan, SHI Jie-hui, et al(赵久然,王荣焕,史洁慧,等). Crops(作物杂志), 2008,35:5. [4] YANG Yu-wen, REN Xiao-ying(杨玉文,任小英). Seed Science & Technology(种子科技), 2010, 28(4): 23. [5] ZHU Wei-hong, QI Jian-shuang, TIE Shuang-gui, et al(朱卫红,齐建双,铁双贵,等). Journal of Henan Agricultural Sciences(河南农业科学), 2007, 10: 33. [6] WANG Feng-ge, ZHAO Jiu-ran, SUN Shi-xian, et al(王凤格,赵久然,孙世贤,等). Journal of Maize Sciences(玉米科学), 2010, 18:2. [7] BAI Qi-lin, CHEN Shao-jiang, YAN Yan-lu, et al(白琪林, 陈绍江, 严衍禄, 等). Scientia Agricultura Sinica(中国农业科学), 2006, 39(7): 1346. [8] LIU Guo-liang, ZHANG Tao, CAO Yan-bo, et al(刘国良, 张 涛, 曹彦波, 等). Modern Scientific Instruments(现代科学仪器), 2006, 6: 37. [9] DUAN Min-xiao, WANG Yuan-dong, GUO Jing-lun,et al(段民孝, 王元东, 郭景伦, 等). Chinese Agricultural Science(中国农学通报), 2004, 20(1): 86. [10] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai, et al(严衍禄,赵龙莲,韩东海,等). Foundation and Application of Near Infrared Spectroscopy Analysis(近红外光谱分析基础与应用). Beijing: China Light Industry Press(北京:中国轻工业出版社), 2005. [11] WANG Shou-jue(王守觉). Acta Electronica Sinica(电子学报), 2002, 30(10): 1417. [12] AN Dong, WANG Ku, WANG Shou-jue(安 冬,王 库,王守觉). Acta Electronica Sinica(电子学报), 2006, 34(2): 277. |
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