| 光谱学与光谱分析 |
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| Rapid and Nondestructive Discrimination of Hybrid Maize Seed Purity Using Near Infrared Spectroscopy |
| HUANG Yan-yan1, ZHU Li-wei1, LI Jun-hui2, WANG Jian-hua1, SUN Bao-qi1, SUN Qun1* |
1. Department of Plant Genetics and Breeding, College of Agriculture and Biotechnology, China Agricultural University /Key Laboratory of Crop Genomics and Genetic Improvement of Ministry of Agriculture／Beijing Key Laboratory of Crop Genetic Improvement, Beijing 100193, China
2. College of Information and Electrical Engineering, China Agricultural University, Beijing 100193, China |
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Abstract Near infrared spectroscopy technology was applied to study rapid and nondestructive discrimination method of hybrid maize seed purity. With NongDa108 hybrid seeds and mother 178 seeds, a discrimination model for the purity of maize single seed was built by near infrared reflectance spectroscopy with distinguished partial least squares (DPLS). A total of 200 seeds including 100 hybrid seeds and 100 mother seeds were divided into two groups: calibration set (150 samples) and validation set (50 samples), and each group had same number of hybrid and mother seeds. To eliminate human errors as much as possible we used two sample cups with transmission hole diameter of 3.0 and 4.5 mm, respectively, at the bottom for spectrum acquisition. The location of sample cups and seeds were fixed during spectrum acquisition process. The result showed that the average identification rate with 3 mm transmission hole diameter was 99.82%, significantly higher than that of 4.5 mm whose average identification rate was just 90.96%. There was no significant difference among the identification rates of one replicate and two replicates spectrum on endosperm face, two replicates spectrum on embryo face and four replicates. The rates of validation set reached about 99%, slightly more than that of one replicate on embryo face. The identification rates of one spectrum and two replicates spectrum on endosperm face in calibration and validation set were 100%, with the spectral region between 4 000 and 8 000 cm-1. With 3.0 mm transmission hole diameter and 4 000～8 000 cm-1 spectral region, the seed purity identification rates in calibration and validation sets built up by one spectrum on endosperm face were 100%. With the increase in principal components, the identification rates in calibration set and validation set gradually increased, and when principal components reached 9, the rate in both of sets were 100%. The results have important value for rapid and nondestructive testing of hybrid maize seed purity.
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Received: 1900-01-01
Accepted: 1900-01-01
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Corresponding Authors:
SUN Qun
E-mail: sqcau@126.com
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[1] BAI Ou, HUANG Rui-dong(白 鸥, 黄瑞冬). Journal of Maize Sciences(玉米科学), 2007, 15(3): 59.
[2] WAN Pu-lin(万普林). Modern Agricultural Science and Technology(现代农业科技), 2005, 7: 13.
[3] WANG Xi, ZHANG Bao-shi(王 玺, 张宝石). Seed(种子), 2002, 21(1): 43.
[4] Delwiche S R. Journal of Cereal Science, 1998, 27: 241.
[5] Farkas J, Dalmadi I. Progress in Agricultural Engineering Sciences, 2009, 5(1): 1.
[6] Yang Zhong, Jiang Ze-hui, Fei Ben-hua et al. Chinese Academy of Forestry, 2005, 41(4): 177.
[7] Norris K H. Journal of Near Infrared Spectroscopy, 1996, 4(1-4): 31.
[8] Sgrulletta D, Stefanis E De. Journal of Food Science, 1997, 9(4): 295.
[9] Yang P, shunk R J, Haken A E, et al. Cereal Chemistry, 2000, 77(1): 44.
[10] Dowell F E. Cereal Chemistry, 2000, 77(1): 155.
[11] KANG Yue-qiong, HAO Feng(康月琼, 郝 风). Seed(种子), 2004, 23(7): 10.
[12] HAN Liang-liang, MAO Pei-sheng, WANG Xin-guo, et al(韩亮亮, 毛培胜, 王新国, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2008, 27(2): 86.
[13] SUN Qun, LI Jun-hui, WANG Jian-hua, et al(孙 群，李军会，王建华，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2009, 29(10): 2669.
[14] Paul Williams, Paul Geladi, Glen Fox, et al. Analytica Chimica Acta, 2009，653(2): 121.
[15] Esteban-Diez I, Gonzalez-Saiz J M, Pizarro C. Analytica Chimica Acta, 2004, 525(2): 171.
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