Abstract
This work investigates the potential use of temperature modulation of MOS gas sensors combined with the Hilbert–Huang transform (HHT) as a feature extraction mechanism for MOS-based electronic noses. Five samples each of ethyl acetate, ethanol and isopropanol were prepared. The response of each of four sensors in an array was decomposed using empirical mode decomposition and the marginal Hilbert spectrum was computed. A set of 72 frequency components was extracted from marginal Hilbert spectrum response of each sensor in an array of four sensor to produce a 288 element fingerprint of each sample. The fingerprints were successfully clustered using PCA and classified using a SVM neutral network.
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