光谱学与光谱分析 |
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Tobacco Quality Analysis of Industrial Classification of Different Producing Area Using Near-Infrared (NIR) Spectrum |
WANG Yi1, MA Xiang1, WEN Ya-dong1, YU Chun-xia1, WANG Luo-ping1, ZHAO Long-lian2, LI Jun-hui2* |
1. Hongta Tobacco(Group) Co., Ltd., Yuxi 653100, China 2. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China |
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Abstract In this study, tobacco quality analysis of industrial classification of different producing area was carried out applying spectrum projection and correlation methods. The group of industrial classification data was near-infrared (NIR) spectrum in 2010 year from different tobacco plant parts and colors of Hongta Tobacco(Group)Co.,Ltd. 6 064 tobacco leaf samples of 17 classes from Yuxi,Chuxiong and Zhaotong, in Yunnan province and 6 industrial classifications were collected using near infrared spectroscopy, which from different parts and colors and all belong to tobacco varieties of K326. The conclusion showed that, the probability of the grading belonging by the first dimension was 84%, the probability of the producing area belonging by the second dimension was 71%. The study can explain the difference of tobacco quality of industrial classification and producing area by a projection method to get the quantitative similarity values. The quantitative similarity values were instructive in combination of tobacco leaf blending.
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Received: 2012-02-08
Accepted: 2012-05-20
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Corresponding Authors:
LI Jun-hui
E-mail: caunir@cau.edu.cn
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[1] ZHANG Tao(章 涛). Guidebook of Cigarette Formulation,Process Technology,Organoleptic Evaluation and Quality Control(卷烟配方设计、生产工艺技术与感官鉴定及质量控制检验实务全书). Hong Kong:China Science,Technology and Culture Press(香港:中国科技文化出版社),2005. [2] LU Yong-heng(陆永恒). Chinese Tobacco Science(中国烟草科学), 2007, 28(3): 43. [3] LIU Guo-shun, FU Yun-peng, et al(刘国顺,符云鹏,等). Tillage and Cultivation(耕作与栽培), 1996, (1): 28. [4] XIE Juan, LUO Jian-qun, YAO He-ming, et al(谢 娟,罗建群,姚鹤鸣,等). Tobacco Science&Technology(烟草科技), 2008, (7): 42. [5] 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. [6] WEN Ya-dong,WANG Yi,WANG Neng-ru,et al(温亚东,王 毅,王能如,等). Acta Tabacaria Sinica(中国烟草学报),2009,15(5):6. [7] GUAN Bin, WANG Jia-jun, ZHANG Jun-song, et al(关 斌,王家俊,张峻松,等). Academic Periodical of Farm Products Processing(农产品加工),2009,(10):102. [8] Chao Tan, Jinyue Wang, Tong Wu, et al. Vibrational Spectroscopy, 2010, 54(1): 35. [9] Yong Zhang, Qian Cong, Yunfei Xie, et al. Spectrochimica Acta Part A, 2008, 71(4): 1408. [10] 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 NIR Spectra 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. 13. [11] Panmanas Sirisombon,Yuki Hashimoto,Munehiro Tanaka. Journal of Food Engineering,2009,93:502. [12] ZHANG Yong, CONG Qian, XIE Yun-fei, et al(张 勇, 丛 茜, 谢云飞,等). Chemical Research in Chinese Universities( 高等学校化学学报 ), 2009, 30(4): 697. [13] Ni Lijun,Zhang Liguo,Xie Juan,et al. Analytica Chimica Acta,2009,633(1):43. [14] Jing Ming, Cai Wensheng, Shao Xueguang. Analytical Letters, 2010, 43: 10.
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