Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic


eISSN: 1178-5608



VOLUME 7 , ISSUE 2 (June 2014) > List of articles


A. Che Soh * / K. K.Chow / U. K. Mohammad Yusuf / A. J. Ishak / M. K. Hassan / S. Khamis

Keywords : electronic nose, artificial neural network, array of sensor, classification, recognition

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 7, Issue 2, Pages 584-609, DOI:

License : (CC BY-NC-ND 4.0)

Received Date : 26-February-2014 / Accepted: 25-April-2014 / Published Online: 27-December-2017



The ability to classify distinctive odor pattern for aromatic plants species provides significant impact in food industry especially for herbs. Each herbs species has a unique physicochemical and a distinctive odors. This project emphasizes on the techniques of artificial intelligence (AI) to distinguish distinctive odor pattern for herbs. Neural Network method has been exploited for the classification and optimization of various odor patterns. Based on AI techniques, Neural Network-based electronic nose system for herbs recognition has been developed. The system consist multi-sensor gas array which detects gas through an increase in electrical conductivity when reducing gases are absorbed on the sensor's surface. The output from individual sensors are collectively assembled and integrated to produce a distinct digital response pattern. A selected sensor array shows its relationship with the aroma of the herbs through the GC-MS test. By using five samples of herbs, the E-nose system has been tested with five different types of sensor. From the results, E-nose system with five sensors has the highest capability in classifying herbs sample. Accuracy in classifying the correct herbs increases with the number of sensors used. This investigation demonstrates that the neural network-based electronic nose technique promises a successful technique in the ability to classify distinctive odor pattern for aromatic herbs species.

Content not available PDF Share



[1] Q-K. Man, C-H. Zheng, X-F. Wang and F-Y. Lin, “Recognition of plant leaves using support vector machine,” Communications in Computer and Information Science. Vol. 15, pp.192-199, 2008.
[2] A.J. Ishak, A. Hussain, and M.M. Mustafa, “Weed image classification using gabor wavelet and gradient field distribution”, Journal of Computers and Electronic in Agriculture (COMPAG), Vol. 66,pp. 53-61, 2009.
[3] L. Zhang, J. Kong, X. Zeng and J. Ren, “Plant species identification based on neural network”, Proceedings of the Fourth International Conference on Natural Computation, 2008.
[4] Z. Zulkifli, Plant Leaf Identification Using Moment Invariants And General Regression Neural Network, MSc.Thesis, Univerisiti Teknologi Malaysia; 2009.
[5] A.J. Ishak, Texture Based Vector Extraction for Weed Recognition, PhD. Thesis, Universiti Kebangsaan Malaysia; 2011.
[6] L. Chen, B. Chen, and Y. Chen, "Image feature selection based on ant colony optimization," AI 2011: Advances in Artificial Intelligence, pp. 580-589, 2011.
[7] Y. Saeys, I. Inza, and P. Larrañaga, "A review of feature selection techniques in bioinformatics," Bioinformatics, Vol. 23, pp. 2507-2517, 2007.
[8] B. Tudu, B. Kow, N. Bhattacharyya and R. Bandyopadhyay ,“Normalization techniques for gas sensor array as applied to classification of black tea”. International Journal on Smart Sensing and Intelligent Systems, Vol. 2, pp.1-14, 2009.
[9] V. O. S. Olunloyo, T.A. Ibidapo and R. R. Dinrifo, “Neural network-based electronic nose for cocoa beans quality assessment”. International Journal of Agricultural Engineering, Vol.13, No.4, pp. 1-17, 2011.
[10] M. Mamat, S. Abdul Samad and M. A. Hannan, “An electronic nose for reliable measurement and correct classification of beverages”, Journal of Sensors, Vol. 11, pp. 6435-6453, 2011.
[11] W. Hidayat, A. Y. Md. Shakaff, M. N. Ahmad and A. H. Adom, “Classification of agarwood oil using an electronic nose”, Journal of Sensors, Vol. 10, pp. 4675-4685, 2010.
[12] N. F. Shilbayeh and M.Z. Iskandarani, “Quality Control of Coffee Using an Electronic Nose System”, American Journal of Applied Sciences, Vol. 1(2), pp 129-135, 2004.
[13] J. Fu, C. Huang, J. Xing and J. Zheng, “Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application” Journal of Sensors, Vol. 12, pp 2818-2830, 2012.
[14] K. Arshak, E. Moore, G.M. Lyons, J. Harris and S. Clifford, A review of gas sensors employed in electronic nose applications, Sensor Review, Vol. 24, pp. 181 – 198, 2004. [15] T. C. Pearce, S. S. Schiffman, H. T. Nagle and J. W.Gardner, Handbook of Machine Olfaction, John Wiley & Sons, 2003.
[16] H. Zwaardemaker and F. Hogewind, “On spray-electricity and waterfall-electricity”, Proc. Acad. Sci. Amst, Vol. 22, pp. 429- 437, 1920. [17] D. W. Alphus and M. Baietto, “Applications and advances in electronic-nose technologies”,Sensors, Vol. 9, pp 5099-5148, May 2009.
[18] K. C. Persaud and G.Dodd, Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose, Nature , Vol. 299, pp. 352-355, 1982.
[19] J.W. Gardner and P.N. Bartlett, “A brief history of electronic noses”, Sensors and Actuator B, Vol. 18-19, pp 211-220, 1994.
[20] K. C. Persaud, S. M. Khaffaf, J. S. Payne, A. M. Pisanelli, D.-H. Lee and H.-G. Byun, “Sensor array techniques for mimicking the mammalian olfactory system,” Sensors and Actuators B, Vol. 36, No. 1, pp. 267-273, 1996.
[21] K.-T. Tang, C.-H. Pan, H.-Y. Hsieh, Y.-S. Liang and S.-C. Liu, "Development of a portable electronic nose system for the detection and classification of fruity odors," Sensors, Vol. 10, pp. 9179-9193, 2010.
[22] P.-M. Lledo, G. Gheusi, and J.-D. Vincent, “Information processing in the mammalian olfactory system,” Physiol Rev, Vol. 85, No. 1, pp. 281-317, 2005.