光谱学与光谱分析 |
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Fast Discrimination of Varieties of Corn Based on Near Infrared Spectra and Biomimetic Pattern Recognition |
SU Qian1, WU Wen-jin1, WANG Hong-wu2, WANG Ku1, 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 100094, China |
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Abstract A new method for fast discrimination of varieties of corn by means of near infrared spectroscopy and biomimetic pattern recognition (BPR) was proposed and the recognition models for seven kinds of corn were built. The experiment adopted 140 samples acquired from seven varieties of corn. Firstly, a field spectroradiometer was used for collecting spectra in the wave number range of 4 000 to 12 000 cm-1. Secondly, the original spectral data were pretreated in order to eliminate noise and improve the efficiency of models, and then the characteristic spectral regions were selected by using fixed-sized moving window evolving factor analysis. Thirdly, principal component analysis (PCA) was used to compress spectral data into several variables, and the cumulate reliabilities of the first five components were more than 99.96%. Finally, according to the first five components, the recognition models were established based on BPR. For the samples in each variety, 10 samples were randomly selected as the training set. The remaining samples of the same variety were used as the first testing set, and the 120 samples of the other varieties were used as the second testing set. Under the condition that almost all the samples in the second set were correctly rejected, the average correct recognition rate was 94.3%. The experimental results demonstrated that the recognition models were effective and efficient. In short, it is feasible to discriminate varieties of corn based on near infrared spectroscopy and BPR.
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Received: 2008-03-30
Accepted: 2008-07-02
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Corresponding Authors:
AN Dong
E-mail: andong@semi.ac.cn
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[1] LI Shang-yu, CHEN Yang, WANG Chun-yan, et al(李尚禹,陈 阳,王春艳,等). Journal of Molecular Science(分子科学学报), 2007, 23(3): 220. [2] 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. [3] 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: Chinese Petrochemical Industry Press(北京:中国石化出版社), 2007. [4] QING Zhao-shen, JI Bao-ping, SHI Bo-lin, et al(庆兆珅,籍保平,史波林,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2008, 28(6): 1273. [5] WANG Tie-gu, LIU Xin-xiang, KU Li-xia, et al(王铁固,刘新香,库丽霞,等). Journal of Maize Sciences(玉米科学), 2008, 16(3): 57. [6] FANG Li-min, LIN Min(方利民,林 敏). Chinese Journal of Analytical Chemistry(分析化学), 2008, 36(6): 815. [7] DING Nian-ya, LI Wei, FENG Xin-wei, et al(丁念亚,黎 薇,冯昕韡,等). Computers and Applied Chemistry(计算机与应用化学), 2008, 25(4): 499. [8] WANG Shou-jue(王守觉). Acta Electronica Sinica(电子学报), 2002, 30(10), 1417. [9] WANG Shou-jue, WANG Bai-nan(王守觉,王柏南). Acta Electronica Sinica(电子学报), 2002, 30(1): 1. [10] CAO Yu, ZHAO Xing-tao(曹 宇,赵星涛). Acta Electronica Sinica(电子学报), 2004, 32(10): 1671. [11] Walczak B, Massart D L. Chemometr. Intell. Lab., 2001, 58: 29. [12] ZHU Xiao-li, XU Yu-peng, LU Wan-zhen(褚小立,许育鹏,陆婉珍). Chinese Journal of Analytical Chemistry(分析化学), 2008, 36(5): 702.
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