Wavelet Based Fuzzy Inference System for Simultaneous Identification and Quantitation of Volatile Organic Compounds Using SAW Sensor Transients
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Calibrated identification of volatile organics by electronic sensors needs development of data collection and data processing methods that can efficiently generate vapor identity features and some quantitative measure of its concentration simultaneously. In this paper, we present a simulation study on this based on surface acoustic wave (SAW) chemical sensors functionalized by polymer coating. The analysis utilizes transient responses of SAW sensors exposed to seven volatile organic compounds at various concentrations. The feature extraction is done by discrete wavelet decomposition using Daubechies-2 basis. A fuzzy c-means clustering method based Sugeno-type fuzzy inference system was then roped in for simultaneous identification and concentration estimation. The performance of the method has been analyzed for various conditions of polymer film thickness. It is concluded that there exists an optimum region for film thickness over which the present method yields nearly 100% correct classification with less than 1% concentration error.
KeywordsWavelet decomposition SAW sensor transients fuzzy clustering and inference quantitative odor recognition electronic nose
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- 2.Rogers, E.K.: Handbook of Biosensors and Electronic Noses: Medicine, Food and Environment. CRC Press (1997)Google Scholar
- 3.Gardner, J.W., Bartlett, P.N.: Electronic Noses: Principles and Applications. Oxford University Press, New York (1999)Google Scholar
- 12.Crank, J.: The Mathematics of Diffusion. Clarendon, Oxford, sec. 4.3 eq. 4.18 (1986)Google Scholar
- 13.Singh, P., Yadava, R.D.S.: Using Parametric Nonlinearity in SAW Sensor Transients and Information Fusion for Improving Electronic Nose Intelligence. Int. J. Computational Intelligence Research 6, 919–927 (2010) (Special Conf. Issue ICCI 2010)Google Scholar
- 14.Singh, P., Yadava, R.D.S.: A Fusion Approach to Feature Extraction by Wavelet Decomposition and Principal Component Analysis in Transient Signal Processing of SAW Odor Sensor Array. Sensors & Transducers J. 126, 64–73 (2011)Google Scholar
- 15.Burrus, C.S., Gopinath, R.A., Guo, H.: Introduction to Wavelets and Wavelet Transforms: A Primer. Prentice-Hall, Englewood Cliffs (1998)Google Scholar
- 18.Nascimento, S., Mirkin, B., Pires, F.: A Fuzzy Clustering Model of Data and Fuzzy c-Means. In: The Ninth IEEE International Conference on Fuzzy Systems, vol. 1, pp. 302–307 (2000)Google Scholar