| 光谱学与光谱分析 |
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| Maize Hybrid Seed Purity Identification Based on Near Infrared Reflectance (NIR) and Transmittance (NIT) Spectra |
| LI Tian-xin1, JIA Shi-qiang2, LIU Xu2, ZHAO Sheng-yi2, RAN Hang2, YAN Yan-lu2, AN Dong2* |
1. College of Civil and Environmental Engineering, University of Sciences and Technology Beijing, Beijing 100083, China
2. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China |
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Abstract This article explore the feasibility of using Near Infrared Reflectance (NIR) and Transmittance (NIT) Spectroscopy (908.1~1 677.2 nm wavelength range) to identify maize hybrid purity, and compare the performance of NIR and NIT spectroscopy. Principle Component Analysis (PCA) and Orthogonal Linear Discriminant Analysis (OLDA ) were used to reduce the dimension of spectra which have been pretreated by first derivative and vector normalization. The hybrid purity identification model of Nonghua101 and Jingyu16 were built by SVM. Models based on NIR spectra obtained correct identification rate as 100% and 90% for Nonghua101 and Jingyu16 respectively. But NIR spectra were greatly influenced by the placement of seeds, and there existed significant difference between NIR spectra of embryo and non-embryo side. Models based on NIT spectroscopy yielded correct identification rate as 98% both for Nonghua101 and Jingyu16. NIT spectra of embryo and non-embryo side were highly similar. The results indicate that it is feasible to identify maize hybrid purity based on NIR and NIT spectroscopy, and NIT spectroscopy is more suitable to analyze single seed kernel than NIR spectroscopy.
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Received: 2014-09-15
Accepted: 2014-12-21
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Corresponding Authors:
AN Dong
E-mail: andong@cau.edu.cn
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[1] http://faostat.fao.org/site/339/default.aspx.
[2] YAN Mi-ge, PIAN Yue-bin, LI Su-ling, et al(闫米格，骈跃斌，李素玲, 等). Journal of Jilin Agricultural Sciences(吉林农业科学)，2011, 36(3): 9.
[3] WU Li-li, WANG Qing-sheng(吴丽丽，王庆胜). Heilongjiang Agricultural Sciences(黑龙江农业科学), 2009, (3)：170.
[4] YAN Yan-lu, CHEN Bin, ZHU Da-zhou, et al(严衍禄，陈 斌，朱大洲, 等). Near Infrared Spectroscopy: Principles, Technologies and Applications(近红外光谱分析的原理，技术与应用). Beijing: China Light Industry Press(北京：中国轻工业出版社)，2013.
[5] 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.
[6] Baye T M, Pearson T C, Settles A M. Journal of Cereal Science，2006, 43: 236.
[7] Smirnov N. The Annals of Mathematical Statistics, 1948, 19(2): 279.
[8] Ye Jieping. Journal of Machine Learning Research. 2005, 6：483.
[9] Cristianini N, Shawe-Taylor J. An Infroduction to Support Vector Machines. Cambridge: Cambridge University Press, 2000. |
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