Abstract
Electronic nose (e-nose) is an instrument that mimics human olfactory system and used to detect, discriminate and classify odours. The instrument applications include food quality assurance, plant disease detection and environmental monitoring. Recent developments in embedded technology have made possible to develop a cost-effective instrument with simple operating procedures. This paper describes the selection of an optimum embedded controller for the development of a hand-held e-nose. The developed instrument uses off-the-shelf components, i.e., sensors, microcontroller and signal conditioning circuit. The data processing utilises multivariate statistical analysis, i.e., principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminate analysis (LDA). The developed instrument was tested to discriminate the basic artificial food essences. Initial results show that the instrument is able to discriminate the samples based on their odour chemical fingerprint profile. The multivariate statistical analysis plots show that the samples are able to be grouped into different clusters.
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