Comparison of Algorithms for an Electronic Nose in Identifying Liquors

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Comparison of Algorithms for an Electronic Nose in Identifying Liquors

Zhi-biao Shi¹; Tao Yu²; Qun Zhao¹; Yang Li³; Yu-bin Lan⁴

1. School of Energy Resources and Mechanical Engineering, Northeast Dianli University, Jilin 132012, P. R. China
2. School of Chemistry Engineering, Northeast Dianli University, Jilin 132012, P. R. China
3. School of Electrical Engineering, Northeast Dianli University, Jilin 132012, P. R. China
4. Aerial Application Technology, USDA-ARS-SPARC-APMRU, College Station, TX 77845, USA

Received:2008-03-12 Revised:2008-05-16 Online:2008-09-30 Published:2008-05-16
Contact: Zhi-biao Shi

Abstract

Abstract: When the electronic nose is used to identify different varieties of distilled liquors, the pattern recognition algorithm is chosen on the basis of the experience, which lacks the guiding principle. In this research, the different brands of distilled spirits were identified using the pattern recognition algorithms (principal component analysis and the artificial neural network). The recognition rates of different algorithms were compared. The recognition rate of the Back Propagation Neural Network (BPNN) is the highest. Owing to the slow convergence speed of the BPNN, it tends easily to get into a local minimum. A chaotic BPNN was tried in order to overcome the disadvantage of the BPNN. The convergence speed of the chaotic BPNN is 75.5 times faster than that of the BPNN.

Key words: electronic nose, liquor, algorithm, principal component analysis

Zhi-biao Shi¹; Tao Yu²; Qun Zhao¹; Yang Li³; Yu-bin Lan⁴. Comparison of Algorithms for an Electronic Nose in Identifying Liquors[J].J4, 2008, 5(3): 253-257.

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Comparison of Algorithms for an Electronic Nose in Identifying Liquors

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