Pattern recognition applied to analysis of gas sensors' array data

For an array of eight chemoresistive gas sensors a computer pattern recognition system was built. Multivariate data analysis was performed for measurements of three gases' dilutions: hydrogen (H₂), methane (CH₄), and carbon monoxide (CO). The pattern recognition system included a feature subset selection algorithm involving PCA and objective function. Dimensionality reduction was applied to two kinds of patterns: three aforementioned gases and six different concentrations of hydrogen. For patterns of the three gases, classification tests were performed using κ-NN algorithm and N-fold based validation method.