Identification and Classification of Flavors in 12 Tobacco Blends Using Electronic Nose

Yi Han1, Yuanxing Duan1, Tao Zhang1, Xia Zhang1, Guangyu Yang1, Xia Meng2, Zhihua Liu1 and Chengming Zhang1,*

1Key Laboratory of Tobacco Chemistry of Yunnan Province, Yunnan Academy of Tobacco Science, Kunming 650106, P.R. China

2Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming 650223, P.R. China

*Corresponding author: Fax: +86 871 68323296; Tel: +86 871 68315280; E-mail:;


Cigarette quality is commonly evaluated on the basis of flavor characteristics of the tobacco blend. Flavor analysis of a cigarette is typically performed by human organoleptic analysis, which is often expensive, less objective and harmful to health. An approach using a metal oxide sensor-based instrument (electronic nose) for headspace analysis was explored as an alternative to human sensory perception for consistent qualitative analysis of flavors in tobacco blends. Chemometric methodologies including principal component analysis, soft independent modeling of class analogy and statistical quality control were used for data processing, identification and classification. The use of the electronic nose technique to qualitatively distinguish among six flavors in 12 tobacco blends was demonstrated. Therefore, the instrument can potentially be used for identifying the raw materials and flavored formulations used for flavoring in cigarette production.


Flavor analysis, Electronic nose, Tobacco blend, Cigarette, Identification, Classification.

Reference (11)


1.      B. Plutowska and W. Wardencki, Food Chem., 101, 845 (2007); doi:10.1016/j.foodchem.2005.12.028.

2.      M. Brattoli, G. De Gennaro, V. De Pinto, A.D. Loiotile, S. Lovascio and M. Penza, Sensors, 11, 5290 (2011); doi:10.3390/s110505290.

3.      A.D. Wilson and M. Baietto, Sensors, 9, 5099 (2009); doi:10.3390/s90705099.

4.      Z. Zhang and G. Li, Microchem. J., 95, 127 (2010); doi:10.1016/j.microc.2009.12.017.

5.      D. Luo, H.G. Hosseini and J. R. Stewart, Sens. Actuators B, 99, 253 (2004);       doi:10.1016/j.snb.2003.11.022.

6.      H.V. Shurmer, J.W. Gardner and H.T. Chan, Sens. Actuators B, 18, 361 (1989); doi:10.1016/0250-6874(89)87042-8.

7.      X. Zhu, Y. Zong, Y. Li and J. Xie, Tob. Sci. Technol., 3, 27 (2008).

8.      P. Ködderitzsch, R. Bischoff, P. Veitenhansl, W. Lorenz and G. Bischoff, Sens. Actuators B, 107, 479 (2005); doi:10.1016/j.snb.2004.11.007.

9.      H. Yu, H. Xu, Q. Ma, G. Zhao and Y. Liu, Acta Tab. Sinica, 31, 63 (2010).

10.  J. Jiang, J. Yang, F. Huang, C. Tang, J. He and P. Sun, Tob. Sci. Technol., 9, 47 (2012).

11. L. Zhu, R.A. Seburg, E. Tsai, S. Puech and J.-C. Mifsud, J. Pharm. Biomed. Anal., 34, 453 (2004); doi:10.1016/S0731-7085(03)00651-4.


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