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Analyzing the Similarity of Different Growing Areas in Red Sun-Cured Tobacco by Using Near Infrared Spectroscopy or Chemical Data |
MA Li1, LI Xue-ying2, DU Guo-rong1, DING Rui3, WANG Yun-bai3, MA Yan-jun1*, LI Jun-hui2* |
1. Beijing Working Station, Technical Center of Shanghai Tobacco Group Corp. Ltd., Beijing 101121, China
2. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
3. Quality and Safety Research Center, Institute of Tobacco Research of Chinese Academy of Agricultural Sciences, Qingdao 266101, China |
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Abstract Near infrared spectroscopy is a comprehensive information collection material, composite information of various substances, but the specific feature is not obvious; the chemical index can reflect the specific characteristics of the material, but the material information is not comprehensive enough. In this paper, total of 115 red sun-cured tobacco samples in 2012 and 2013 were tested, which were from Guizhou, Hunan, Jilin, Jiangxi, Shandong, Sichuan. After near infrared spectroscopy was processed by the first derivative and SG smooth and 26 items of chemical data and 26 items of calculated data (for example, ratio of sugar to nicotine, etc.) were normalized, the PPF (Projection of Basing on Principal Component and Fisher Criterion) projection method was used to analyze the similarity and substitution of growing areas in red sun-cured tobacco. Combined with the analysis of near infrared spectroscopy and chemical indicators, the results showed that the similarity analysis result by using near infrared spectroscopy is in basic agreement with the result by using chemical data, which means two methods can both analyze the similarity of growing areas. Nitrosamines would determine the growing areas as an important indicator by analyzing the variance contribution rate of 52 test data. The similarity of small areas was analyzed by used near infrared spectroscopy and the result was that the part of different small areas can be replaced. Application of near infrared spectroscopy technology can quickly and accurately analyze the similarity and substitution of different growing areas. Combining chemical data can be used to analyze its intrinsic characteristics, which can play an important role in the scientific exploitation of tobacco raw materials.
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Received: 2016-10-26
Accepted: 2017-05-10
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Corresponding Authors:
MA Yan-jun, LI Jun-hui
E-mail: 13366036175@189.cn;caunir@cau.edu.cn
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[1] DU Wen, YI Jian-hua, TAN Xin-liang, et al(杜 文, 易建华, 谭新良, 等). Acta Tabacaria Sinica(中国烟草学报), 2009, 15(5): 1.
[2] Peerapattana J, Shinzawa H, Otsuka K, et al. J. Near Infrared Spectroscopy, 2013, 21(3): 195.
[3] Liu F, Tang X. Bioengineered, 2015, 6(3): 166.
[4] Graham S F, Haughey S A, Ervin R M, et al. Food Chemistry, 2012, 132(3): 1614.
[5] Xu H, Qi B, Sun T, et al. Journal of Food Engineering, 2012, 109(1): 142.
[6] 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.
[7] Xu F, Yu J, Tesso T, et al. Applied Energy, 2013, 104: 801.
[8] Haughey S A, Graham S F, Cancouёt E, et al. Food Chemistry, 2013, 136(3): 1557.
[9] 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 Spectroscopy(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.
[10] Jolliffe I T. Principal Component Analysis, Springer Series in Statistics, 2nd ed. Springer, NY, USA,2002.
[11] 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. |
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