玉米品种近红外光谱的特征分析与鉴别方法

doi:10.3964/j.issn.1000-0593(2010)12-3213-04

摘要
参考文献
相关文章 (15)

全文: PDF (1394 KB)
输出: BibTeX | EndNote (RIS)

摘要：以玉米种子的4 000～12 000 cm^-1波段的漫反射近红外光谱为研究对象，提出了一种鉴别玉米品种的新方法。采用主成分分析法(PCA)来研究数据特征，发现近红外光谱在特征空间中具有显著的长条状分布特征，为此我们研究了改变样本点在PCA空间中的分布对品种鉴别的影响，并提出了归一化主成分分析(NPCA)的特征提取算法，同时还根据近红外光谱的数据分布特点提出了一种主方向仿生模式识别的分类算法，进一步提高了鉴别正确率。鉴别模型对第一测试集的平均正确识别率达到了97.67%，平均正确拒识率达到了98.40%，30个品种中的13个达到了100%的正确识别率;对第二测试集的平均正确拒识率达到了98.90%，有11个品种达到了100%的正确拒识率，具有较高的鉴别准确度。

关键词：近红外光谱;主成分分析;仿生模式识别;品种鉴别

Abstract：A new method for the discrimination of varieties of corn was proposed based on the data set of near-infrared spectroscopy range from 4 000 to 12 000 cm^-1 of corn seed varieties. Principal component analysis (PCA) method was used to study the feature of the data, and the authors found that the near-infrared spectroscopy of corn seed varieties has a clear feature of zonal distribution, so the correlativity between the change in the distribution of the principal component and the discrimination result was studied, according to which the normalized principal component analysis (NPCA) method was proposed. Besides, principal direction biomimetic pattern recognition (PBPR) was proposed according to the feature, which got a better discrimination result. The average correct recognition rate attained 97.67% for test set Ⅰ, and the average correct rejection rate attained 98.40%, with 13 of the 30 varieties reaching the correct recognition rate of 100%; The average correct rejection rate attained 98.90% for the test set Ⅱ, and 11 of the 30 varieties reached the correct rejection rate of 100%. It was proved that the method had a high correct discrimination rate.

Key words：Near-infrared spectroscopy;Principal component analysis;Biomimetic pattern recognition;Variety discrimination

收稿日期: 2010-02-04 修订日期: 2010-05-08

中图分类号:

O433.4

通讯作者: 李卫军 E-mail: wjli@semi.ac.cn

引用本文:

王徽蓉，陈新亮，李卫军^*，来疆亮 . 玉米品种近红外光谱的特征分析与鉴别方法[J]. 光谱学与光谱分析, 2010, 30(12): 3213-3216.
WANG Hui-rong, CHEN Xin-liang, LI WEI-jun^*, LAI Jiang-liang . Feature Analysis and Discrimination of Varieties of Corn Based on Near Infrared Spectra . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2010, 30(12): 3213-3216.

链接本文:

https://www.gpxygpfx.com/CN/10.3964/j.issn.1000-0593(2010)12-3213-04 或 https://www.gpxygpfx.com/CN/Y2010/V30/I12/3213

[1] 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 Petrochemical Press(北京：中国轻工业出版社), 2005.
[2] LU Wan-zhen, YUAN Hong-fu, XU Guang-tong, et al(陆婉珍，袁洪福，徐广通，等). Modern Near Infrared Spectroscopy Analytical Technology(Second Edition)(现代近红外光谱分析技术，第2版). Beijing: China Petrochemical Press(北京：中国石化出版社), 2007.
[3] FANG Li-min, LIN Min(方利民，林敏). Chinese Journal of Analytical Chemistry(分析化学), 2008, 36(6): 815.
[4] HUANG Min, HE Yong, HUANG Ling-xia，et al(黄敏，何勇，黄凌霞，等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2006, 25(5): 342.
[5] WANG Shou-jue(王守觉). Acta Electronica Sinica(电子学报), 2002, 30(10): 1417.
[6] WANG Shou-jue, WANG Bai-nan(王守觉，王柏南). Acta Electronica Sinica(电子学报), 2002, 30(1): 1.
[7] YANG Guo-wei, WANG Shou-jue, LIU Yang-yang(杨国为，王守觉，刘扬阳). Acta Electronica Sinica(电子学报), 2008, 36(12): 2490.
[8] LI Han, WU Yan(李晗，武妍). Computer Engineering and Applications(计算机工程与应用), 2009, 45(3): 181.
[9] WANG Shou-jue, QU Yan-feng, LI Wei-jun(王守觉，曲延锋，李卫军). Acta Electronica Sinica(电子学报), 2004, 32(7): 1057.
[10] LIU Yan-li, CAO Kui(刘艳丽，曹奎). Computer Applications and Software(计算机应用与软件), 2003, 20(4): 40.
[11] CHEN Fu-bing, CHEN Xiu-hong, WANG Wen-sheng(陈伏兵，陈秀宏，王文胜). Computer Engineering and Applications(计算机工程与应用), 2005, 41(34): 34.
[12] ZHANG Wen-feng, SHI Zhong-ke(张文峰，史忠科). Computer Engineering and Applications(计算机工程与应用), 2007, 43(36): 246.