Discrimination and Interpretation of Electronic Nose Data Using Ica
نویسنده
چکیده
This work reports on independent component analysis (ICA) as a tool used to discriminate between odour signals. Measurements of six different alcohol solutions were carried out with a commercial gas sensor array system, a so called electronic nose. The solutions were made of either pure propanol or butanol at concentration levels of 0.5%, 1% and 2%. Principal component analysis (PCA), a standard multivariate analysis tool for gas sensor data, needed three principal components (PC) for effective discrimination of the solutions. With ICA, only two independent components (IC) were needed to achieve a similar result. PC-1 and IC-1 gave both meaningful representations of alcohol concentrations in the solutions. However, only a combination of PC2 and PC3 could represent different types of alcohols as effectively as IC2 did.
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