Study on the Projection Method Based on Principal Component of Tobacco Near-Infrared Spectrum and Application in Redrying Model
TAO Shuai1,MA Xiang2,LI Jun-hui1*,ZHANG Wen-juan1,ZHAO Long-lian1,WEN Ya-dong2,WANG Yi2,ZHANG Lu-da3
1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100193, China 2. Technology Center of Yuxi Hongta Group, Yuxi 653100, China 3. College of Science, China Agricultural University, Beijing 100193, China
Abstract：The present paper briefly describes the application of near-infrared spectroscopy technology in tobacco. Two methods for solving projection vector based on the principal component of near-infrared spectrum are reported. They are named as projection of basing on principal component and Fisher criterion by principal component analysis method (PPF_PCA for short) and projection of basing on principal component and Fisher criterion by solving eigenvalue (PPF_Eig for short), and they are studied and compared in the application and evaluation of the redrying model. The result of the first-dimensional projection on 9 kinds of tobacco leaf grading samples shows that, the diversity of the first-dimensional projection values of inter-class and intra-class by the PPF_PCA method is both larger than that by the PPF_Eig method, and the mean absolute deviation of the mean projection values of inter-class by the PPF_PCA method is about 1.26 times that of the PPF_Eig method. At the same time, this result is interpreted by using the contribution rate of the first-dimensional projection values. That is, the contribution rate of first-dimensional projection values by the PPF_PCA method is 93%, while the contribution rate of first-dimensional projection values by the PPF_Eig method is 77%. The former is about 1.21 times the later. Therefore, the first-dimensional projection values by PPF_PCA method include more information of diversity of both inter-class and intra-class. The similarity of samples inter-class and the diversity of samples intra-class can be evaluated more objectively from first-dimensional projection figure(on 9 kinds of tobacco leaf grading samples, 33 kinds of tobacco leaf grading samples and 6 redrying blending models), so it is more convenient to be used as a reference for the redrying model of tobacco, and it has a good application prospect in other formulation design of agricultural products (traditional Chinese medicine etc.)
Key words：Tobacco leaf;Near-infrared;Principal component;Projection;Redrying model
陶 帅1，马 翔2，李军会1*，张文娟1，赵龙莲1，温亚东2，王 毅2，张录达3 . 基于烟叶近红外光谱主成分数据的投影方法研究及其在复烤配方中的应用[J]. 光谱学与光谱分析, 2009, 29(11): 2970-2974.
TAO Shuai1,MA Xiang2,LI Jun-hui1*,ZHANG Wen-juan1,ZHAO Long-lian1,WEN Ya-dong2,WANG Yi2,ZHANG Lu-da3. Study on the Projection Method Based on Principal Component of Tobacco Near-Infrared Spectrum and Application in Redrying Model . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2009, 29(11): 2970-2974.
[1] MA Xiang, WEN Ya-dong, WANG Yi, et al(马 翔, 温亚东, 王 毅, 等). Tobacco Science & Technology(烟草科技)，2007(3)：16. [2] Hildrum K I, Isaksson T, Naes T, et al. Near-Infrared Spectroscopy Bridging the Gap between Cata Analysis and Appliction. New York: Ellis Horwood, 1994. 153. [3] ZHANG Jian-ping, XIE Wen-yan(张建平，谢雯燕). Tobacco Science & Technology(烟草科技)，1999, (3)：37. [4] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai, et al(严衍禄，赵龙莲, 韩东海, 等). Basis and Application of Near Infrared Spectral 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. 12. [5] WANG Dong-dan, LI Tian-fei, WU Yu-ping, et al(王东丹, 李天飞, 吴玉萍, 等). Journal of Yunnan University(云南大学学报), 2001, 23(2): 135. [6] SHU Ru-xin, WANG Guo-dong, ZHANG Jian-ping, et al(束茹欣，王国东，张建平, 等). Tobacco Science & Technology(烟草科技)，2006(8)：12. [7] ZHANG Jian-ping, CHEN Jiang-hua, SHU Ru-xin, et al(张建平，陈江华，束茹欣，等). Acta Tabacaria Sinica(中国烟草学报)，2007, 13(5)：1. [8] WANG Jia-jun, LI Juan(王家俊, 李 娟). Tobacco Science & Technology(烟草科技)，2008, (3)：5. [9] BIAN Zhao-qi，ZHANG Xue-gong(边肇祺，张学工). Pattern Recognition(模式识别). Beijing：Tsinghua University Press(北京：清华大学出版社)，2000. [10] Wilks S S. Mathematical Statistics. New York: Wiley Press, 1962. [11] Duda R, Hart P. Pattern Classification and Scene Analysis. New York: Wiley Press, 1973. [12] Tian Q. Optical Engineering, 1986, 25(7): 834. [13] Jin Z, Yang J Y, Hu Z S, et al. Pattern Recognition, 2001, 34(7): 1405.
