| 光谱学与光谱分析 |
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| Study on Spectral Calibration of Discrimination of Corn Variety Using Near-Infrared Spectra Based on DS Algorithm |
| LIU Pei-zhong1, ZHANG Li-ping2*, LI Wei-jun2, QIN Hong2, DONG Xiao-li2 |
1. College of Engineering, Huaqiao University, Quanzhou 362000, China
2. Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China |
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Abstract From the perspective of calibration, the present paper studies the model stability problem in qualitative analysis of NIR. Aiming at the issue of model failure caused by different data acquisition time, 13 varieties of corn were used as experimental material, and learning from the idea of model calibration transfer between the two instruments in quantitative analysis of NIR, the DS(direct standardization ) algorithm was used to calibrate the spectra acquired at different times with the same instrument, that made the varieties identification model established one time able to be applied to identify the test data at different acquisition time. First, transfer set was selected from the master spectrum set by Kennard/Stone algorithm, the corresponding number spectrums in slave spectrum set were selected, and then DS algorithm was applied to transfer set to calculate the transformation function between the two sets of data. Finally, the remaining slave spectrums were transformed so that they could apply to the model. This study does some experiment to discuss the impact of the number of transfer set and the location of calibration on the calibration results. Respectively, the experiment results were analyzed from two aspects, one is the correct discrimination rate in qualitative analysis, and the other is the distribution distance between master spectrums and slave spectrums before and after calibration. The experiment results indicate that this approach is effective to solve the spectra drift produced by sampling over time, can bring higher recognition rate on different sampling time test sets, also improves the robustness and application scope of the identification model, and the experiment results also indicate that the best result can be obtained with calibration locating after feature extraction.
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Received: 2013-08-08
Accepted: 2013-12-04
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Corresponding Authors:
ZHANG Li-ping
E-mail: zliping@semi.ac.cn
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[1] 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.
[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] SHAO Yong-ni, CAO Fang, et al(邵咏妮，曹 芳，等). J. Infrared Millim. Waves(红外与毫米波学报), 2007, 26(6): 433.
[4] LIANG Liang, LIU Zhi-xiao, YANG Min-hua, et al(梁 亮，刘志宵，杨敏华，等). J. Infrared Millim. Waves(红外与毫米波学报), 2009, 28(5): 353.
[5] Mamani-Linares L W, Gallo C, et al. Meat Science, 2012, 90: 378.
[6] ZHANG Li-ping, LI Wei-jun, WANG Ping, et al(张丽萍，李卫军，王 平，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(10): 2785.
[7] Liu X, Han L J, Yang Z L. Journal of Dainy Science, 2011, 94(11): 5599.
[8] Fan Wei, Liang Yizeng, Yuan Dalin, et al. Analytica Chimica Acta, 2008, 623: 22
[9] Hu Runwen, Xia Junfang. 2011 International Conference on Electric Information and Control Engineering (ICEICE). 2011, 5(27): 3062.
[10] 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 Applictions(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.
[11] Kennard R W, Stone L A. Technometrics, 1969, 11: 137.
[12] WANG Shou-jue(王守觉). Acta Electronica Sinica(电子学报), 2002, 30(10):1417.
[13] WANG Shou-jue, LAI Jiang-liang(王守觉，来疆亮). First Step to Multi-Dimensional Space Biomimetic Informatics(多维空间仿生信息学入门). Beijing: National Defense Industry Press(北京：国防工业出版社), 2008. |
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