Discrimination between Chinese Jing Wine and Counterfeit Using Different Signal Features of an Electronic Nose


Because sensory analysis and chromatographic analysis were not well suitable for the discrimination between Chinese Jing wines and counterfeits, an electronic nose (in short, eNose) was employed to carry out the task. In the investigation three kinds of features of eNose signals were extracted and as input data of principal component analysis (PCA). These features are named as mean-differential coefficient value (MDCV), energy value of wavelet packet decomposition (WE) and relative steady-state response value (RSV), respectively. The results demonstrated that the discrimination based on these features data could all be performed by PCA, and the RSV was the best. At the same time, an evaluation method was proposed to evaluate the discrimination capability of these features quantitatively, and the evaluation results are basically in accord with PCA discrimination results. This showed the evaluation method was appropriate for evaluating the discrimination capability of different features. In conclusion, the investigation indicated that the eNose coupled with PCA was absolutely competent for the discrimination tasks, and especially the feature RSV was simple and reliable.

Share and Cite:

Y. Yin, H. Yu and H. Zhou, "Discrimination between Chinese Jing Wine and Counterfeit Using Different Signal Features of an Electronic Nose," Journal of Sensor Technology, Vol. 2 No. 3, 2012, pp. 109-115. doi: 10.4236/jst.2012.23016.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] J. Lozano, J. P. Santos and M. C. Horrillo, “Enrichment Sampling Methods for Wine Discrimination with Gas Sensors,” Journal of Food Composition and Analysis, Vol. 21, No. 8, 2008, pp. 716-723.
[2] I. M. Apetrei, M. L. Rodríguez-Méndez, C. Apetrei, I. Nevares, M. Del Alamo and J. A. De Saja, “Monitoring of Evolution during Red Wine Aging in Oak Barrels and Alternative Method by Means of an Electronic Panel Test,” Food Research International, Vol. 45, No. 1, 2012, pp. 244-249. doi: 10.1016/j.foodres.2011.10.034
[3] I. Concina, M. Bornsek, S. Baccelliere, M. Falasconi, E. Gobbi and G. Sberveglieri, “Alicyclobacillus spp.: Detection in Soft Drinks by Electronic Nose,” Food Research International, Vol. 43, No. 8, 2010, pp. 2108-2114. doi: 10.1016/j.foodres.2010.07.012
[4] J. Lozano, J. P. Santos and M. C. Horrillo, “Classification of White Wine Aromas with an Electronic Nose,” Talanta, Vol. 67, No. 3, 2005, pp. 610-616.
[5] H. Qin, D. Huo, L. Zhang, L. Yang, S. Zhang, M. Yang, C. Shen and C. Hou, “Colorimetric Artificial Nose for Identification of Chinese Liquor with Different Geographic Origins,” Food Research International, Vol. 45, No. 1, 2012, pp. 45-51. doi: 10.1016/j.foodres.2011.09.008
[6] J. A. Ragazzo-Sanchez, P. Chalier, D. Chevalier, M. Calderon-Santoyo and C. Ghommidh, “Identification of Dif-Ferent Alcoholic Beverages by Electronic Nose Coupled to GC,” Sensors and Actuators B, Vol. 134, No. 1, 2008, pp. 43-48. doi: 10.1016/j.snb.2008.04.006
[7] J. P. Santos, M. J. Fernandez, J. L. Fontecha, J. Lozano, M. Aleixandre, M. García, J. Gutiérrez and M. C. Horrillo, “SAW Sensor Array for Wine Discrimination,” Sensors and Actuators B, Vol. 107, No. 1, 2005, pp. 291-295.
[8] L. Carmel, S. Levy, D. Lancet and D. Harel, “A Feature Extraction Method for Chemical Sensors in Electronic Nose,” Sensors and Actuators B, Vol. 93, No. 1-3, 2003, pp. 67-76. doi: 10.1016/S0925-4005(03)00247-8
[9] M. Padilla, I. Montoliu, A. Pardo, A. Perera and S. Marco, “Feature Extraction on Three Way Enose Signals,” Sensors and Actuators B, Vol. 116, No. 1-2, 2006, pp. 145-150. doi: 10.1016/j.snb.2006.03.011
[10] R. Haddad, L. Carmel and D. Harel, “A Feature Extraction Algorithm for Multi-Peak Signals in Electronic Noses,” Sensors and Actuators B, Vol. 120, No. 2, 2007, pp. 467-472. doi:10.1016/j.snb.2006.02.048
[11] A. Leone, C. Distante, N. Ancona, K. C. Persaud, E. Stella and P. Siciliano, “A Powerful Method for Feature Extraction and Compression of Electronic Nose Responses,” Sensors and Actuators B, Vol. 105, No. 2, 2005, pp. 378-392. doi: 10.1016/j.snb.2004.06.026
[12] S. Panigrahi, S. Balasubramanian, H. Gu, C. Logue and M. Marchello, “Neural-Network-Integrated Electronic Nose System for Identification of Spoiled Beef,” LWT— Food Science and Technology, Vol. 39, No. 2, 2006, pp. 135-145. doi: 10.1016/j.lwt.2005.01.002
[13] Y. Yin and X. Tian, “Classification of Chinese Drinks by a Gas Sensors Array and Combination of the PCA with Wilks Distribution,” Sensors and Actuators B, Vol. 124, No. 2, 2007, pp. 393-397.
[14] Y. Yin, H. Yu and H. Zhang, “A Feature Extraction Method Based on Wavelet Packet Analysis for Discrimination of Chinese Vinegars Using a Gas Sensors Array,” Sensors and Actuators B, Vol. 134, No. 2, 2008, pp. 1005-1009.
[15] L. Pillonel, J. O. Bosset and R. Tabacchi, “Rapid Preconcentration and Enrichment Techniques for the Analysis of Food Volatile. A Review,” LWT—Food Science and Technology, Vol. 35, No. 1, 2002, pp. 1-14.
[16] F. Doymaz, J. A. Romagnoli and A. Palazoglu, “A Strategy for Detection and Isolation of Sensor Failures and Process Upsets,” Chemometrics and Intelligent Laboratory Systtems, Vol. 55, No. 1-2, 2001, pp. 109-123. doi: 10.1016/S0169-7439(00)00126-X
[17] S. Capone, P. Siciliano, F. Quaranta, R. Rella, M. Epifani and L. Vasanelli, “Analysis of Vapours and Foods by Means of an Electronic Nose Based on a Sol-Gel Metal Oxide Sensors Array,” Sensors and Actuators B, Vol. 69, No. 3, 2000, pp. 230-235. doi: 10.1016/S0925-4005(00)00496-2
[18] Z. Bian and X. Zhang, “Pattern Recognition,” 2nd Edition, Publishing House of Tsinghua University, Beijing, 2000, pp. 178-179.

Copyright © 2022 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.