Application of PSO Clustering for Selection of Chemical Interface Materials for Sensor Array Electronic Nose

Abstract

In this study PSO has been applied for mining the thermodynamic data on vapor–polymer solvation interactions. The goal of this study is to make polymer selection for the surface acoustic wave (SAW) chemical sensors for electronic nose applications. An electronic nose sensor array is required to generate varying signal patterns corresponding to different vapor types, and the sensor array data is analyzed by pattern recognition methods for extracting specific vapor identities. In this work we considered a specific detection problem, namely, the detection of freshness or spoilage states of fish as food product. Considering the solvation data for 26 potential polymers and 17 likely vapors in the headspace of fish samples, the application of PSO resulted in a set of six polymers for defining the SAW sensor array. The PSO selection was validated by generating simulation data based on a SAW sensor model and analyzing the 6-element sensor array patterns by principal component analysis (PCA).