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鉴别玉米杂交种纯度的近红外光谱分析技术研究

Near Infrared Spectroscopy Analysis Method of Maize Hybrid Seed Purity Discrimination

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摘要

以不同产地和年份的农华101(NH101)玉米杂交种和母本种子为对象, 研究了鉴别玉米杂交种子纯度的近红外光谱分析方法。 光谱采集时间跨度达10个月, 运用傅里叶变换(FT)近红外光谱仪器, 在不同季节用23天(分五个时间段)采集了这些样品共920条玉米单子粒近红外漫反射光谱。 全部原始光谱用移动窗口平均、 一阶差分导数和矢量归一化进行预处理, 使用主成分分析(PCA)方法和线性判别分析(LDA)方法降维, 采用仿生模式识别(BPR)方法建立模型。 通过对光谱预处理校正光谱失真, 使样品光谱集在特征空间分布的范围收缩, 相对距离增大了近70倍, 实现了母本和杂交种子的鉴别。 通过代表性样品的选择, 提高了模型对光谱采集时间、 地点、 环境等条件变动的应变能力, 也提高了模型对样品种子制种时间与地点变动的应变能力, 增强了模型的稳健性, 使测试集玉米单子粒杂交种和母本种子的平均正确识别率达到95%以上, 而平均正确拒识率也达到85%以上。

Abstract

Near infrared spectroscopy analysis method of discrimination of maize hybrid seed purity was studied with the sample of Nong Hua 101 (NH101) from different origins and years. Spectral acquisition time lasted for 10 months. Using Fourier transform (FT) near infrared spectroscopy instruments, including 23 days in different seasons (divided into five time periods), a total of 920 near infrared diffuse reflectance spectra of single corn grain of those samples were collected. Moving window average, first derivative and vector normalization were used to pretreat all original spectra, principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to reduce data dimensionality, and the discrimination model was established based on biomimetic pattern recognition (BPR) method. Spectral distortion was calibrated by spectra pretreatment, which makes characteristics spatial distribution range of sample spectra set contract. The relative distance between hybrid and female parent increased by nearly 70-fold, and the discrimination model achieved the identification of hybrid and female parent seeds. Through the choice of representative samples, the model's response capacity to the changes in spectral acquisition time, place and environment, etc. was improved. Besides, the model's response capacity to the changes in time and site of seed production was also improved, and the robustness of the model was enhanced. The average correct acceptance rate (CAR) of the test set reached more than 95% while the average correct rejection rate (CRR) of the test set also reached 85%.

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中图分类号:O657.3

DOI:10.3964/j.issn.1000-0593(2014)05-1253-06

基金项目:国家公益性行业科研专项项目(201203052)和北京市科技计划项目(D131100000413002)和大北农青年学者研究计划项目(1081-2413001)资助

收稿日期:2013-07-08

修改稿日期:2013-11-25

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作者单位    点击查看

黄华军:中国农业大学信息与电气工程学院, 北京100083
严衍禄:中国农业大学信息与电气工程学院, 北京100083
申兵辉:中国农业大学信息与电气工程学院, 北京100083
刘哲:中国农业大学信息与电气工程学院, 北京100083
顾建成:北京金色农华种业科技有限公司, 北京100080
李绍明:中国农业大学信息与电气工程学院, 北京100083
朱德海:中国农业大学信息与电气工程学院, 北京100083
张晓东:中国农业大学信息与电气工程学院, 北京100083
马钦:中国农业大学信息与电气工程学院, 北京100083
李林:中国农业大学信息与电气工程学院, 北京100083
安冬:中国农业大学信息与电气工程学院, 北京100083

联系人作者:黄华军(hhj_55331@126.com)

备注:黄华军, 1989年生, 中国农业大学信息与电气工程学院研究生

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引用该论文

HUANG Hua-jun,YAN Yan-lu,SHEN Bing-hui,LIU Zhe,GU Jian-cheng,LI Shao-ming,ZHU De-hai,ZHANG Xiao-dong,MA Qin,LI Lin,AN Dong. Near Infrared Spectroscopy Analysis Method of Maize Hybrid Seed Purity Discrimination[J]. Spectroscopy and Spectral Analysis, 2014, 34(5): 1253-1258

黄华军,严衍禄,申兵辉,刘哲,顾建成,李绍明,朱德海,张晓东,马钦,李林,安冬. 鉴别玉米杂交种纯度的近红外光谱分析技术研究[J]. 光谱学与光谱分析, 2014, 34(5): 1253-1258

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