Preprocessing of SAW Sensor Array Data and Pattern Recognition

Abstract

This paper discusses data preprocessing methods for feature extraction in polymer coated surface acoustic wave (SAW) vapor sensor array. The role of sensor response modeling is explored in developing an appropriate preprocessing strategy. The preprocessor should be designed to transform the experimentally measured data variables (sensor signals) into a format that relates the characteristic features of the object for recognition (vapor) linearly with the transformed data variables. This facilitates generation of greater dispersion in feature space defined by linear feature extraction method such as principal component analysis. Considering solvation parameters of the vapor molecules to be their characteristic descriptors (features) and prompted by the equilibrium response model of SAW sensors, it is demonstrated that by transforming the raw data space logarithmically generates greater dispersion in feature space defined by the principal component analysis, and enhances classification efficiency of the backpropagation neural network substantially.