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Vol. 147, No. 12, December 2012, pp. 108-128

 

Bullet

 

Data Mining Approach to Polymer Selection for Making SAW Sensor Array Based Electronic Nose
 
Sunil K. JHA and R. D. S. YADAVA

Sensor & Signal Processing Laboratory

Department of Physics, Faculty of Science

Banaras Hindu University, Varanasi 221005, India

Tel.: +91-542-2307308, fax: +91-542-2368390

E-mail: ardius@gmail.com, ardius@bhu.ac.in

 

Received: 7 September 2012   /Accepted: 18 December 2012   /Published: 31 December 2012

Digital Sensors and Sensor Sysstems

 

Abstract: In this paper, a simple application of principal component analysis and hierarchical clustering methods of classical data mining has been demonstrated for making selection of polymer coatings for surface acoustic wave (SAW) sensor array. The database consists of thermodynamic solvation parameters of the prospective polymers and the target vapors. The linear-solvation-energy-relationship (LSER) has been used to calculate partition coefficients from the solvation parameters. The partition coefficient data for various vapor-polymer combinations is then arranged as a data matrix taking the polymers for instances (rows) and the vapors for variables (columns). Selection of polymer subset for optimal discrimination of target vapors from background interferents has been made by analyzing the principal component score and load plots. A simulation study for detection of trinitrotoluene (TNT) and dinitrotoluene (DNT) explosive vapors camouflaged in the background of 29 interferent organic vapors originating from varied sources such as soil, vegetation, body odor, and indoor and outdoor industrial/commercial environments has been carried out. The paper demonstrates that the application of data mining methods for the selection of polymeric sensor coatings could prove prudent in terms of reducing system complexity, cost and development time.

 

Keywords: Polymer selection, Data mining, SAW sensor array, Electronic nose, TNT/DNT vapor detection

 

 

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