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Application of Fuzzy c-Means Clustering for Polymer Data Mining for Making SAW Electronic Nose

  • Prabha Verma
  • R. D. S. Yadava
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 247)

Abstract

Polymers provide chemical interfaces to electronic nose sensors for detection of volatile organic compounds. An electronic nose combines a properly selected set of sensors (array) with pattern recognition methods for generating information rich odor response patterns and extracting chemical identities. Each sensor in the array is functionalized by a different polymer for diversely selective sorption of target chemical analytes. Selection of an optimal set of polymers from a long list of potential polymers is crucial for cost-effective high performance development of electronic noses. In this paper we present an application of fuzzy c-means clustering on partition coefficient data available for target vapors and most prospective polymers for selection of minimum number of polymers that can create maximum discrimination between target chemical compounds. The selection method has been validated by simulating a polymer coated surface acoustic wave (SAW) sensor array for monitoring fish freshness.

Keywords

Fuzzy c-means clustering polymer selection SAW electronic nose fish quality monitoring pattern recognition 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Sensor & Signal Processing Laboratory, Department of Physics, Faculty of ScienceBanaras Hindu UniversityVaranasiIndia

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