Solvation, transduction and independent component analysis for pattern recognition in SAW electronic nose

This paper revisits the fundamental basis for signal generation in polymer coated SAW vapor sensors and applies the independent component analysis (ICA) for feature extraction from the SAW sensor array data to explore whether the independent components could represent analyte-specific solvation parameters and whether they could form the feature vector for reliable pattern classification. Thermodynamic partitioning of analytes between vapor and polymer phases is treated as independent contributions from different solvation mechanisms, each associated with characteristic ‘environment swap’ energy. The overall equilibrium partition coefficient of an analyte is modeled as product of partial partition coefficients associated with different solvation mechanisms. The polymer films on SAW devices are treated to be acoustically thin. The theory of signal generation accounts for effects from both the mass as well as the viscoelastic loadings. It explains the signal amplification factor due to viscoelastic effects, and models the sensor signal to be proportional to the equilibrium partition coefficient. Thus, the logarithmic signal becomes a linear combination of the partial free energies associated with various solvation mechanisms. A linear-solvation-energy relationship (LSER) like factorization is assumed for the partial free energies where the latter are expressed as product of analyte and complimentary polymer associated solvation parameters. The problem of sensor array signal analysis is then treated as a blind source separation problem with the analyte solvation parameters being the independent sources, the polymer solvation parameters being the mixing weights and the log(signals) being the measured variables. The FastICA algorithm with Gram-Schmidt orthogonalization is applied to determine independent components. The principal component analysis (PCA) is done as pre-processing step for ICA. An experimental SAW sensor array data available in the literature [Rose-Pehrsson et al., Anal. Chem. 60 (1988) 2801–2811] is used to seek validation for our approach, and to examine the role of ICA in SAW sensor array signal processing. In brief, the paper establishes a direct relationship between the independent components and the analyte solvation parameters, and presents ICA as an effective method for feature extraction for pattern recognition in SAW electronic noses.