Abstract
We discuss a biomimetic approach for improving odour detection in artificial olfactory systems that utilises temporal dynamical delivery of odours to chemical sensor arrays deployed within stationary phase materials. This novel odour analysis technology, which we have termed an artificial mucosa, uses the principle of “nasal chromatography”; thus emulating the action of the mucous coating the olfactory epithelium. Temporal segregation of odorants due to selective phase partitioning during delivery in turn gives rise to complex spatio-temporal dynamics in the responses of the sensor array population, which we have exploited for enhanced detection performance. We consider the challenge of extracting stimulus-specific information from such responses, which requires specialised time-dependent signal processing, information measures and classification techniques.
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Pearce, T.C., Sánchez-Montañés, M.A., Gardner, J.W. (2009). Improved Odour Detection through Imposed Biomimetic Temporal Dynamics. In: Gutiérrez, A., Marco, S. (eds) Biologically Inspired Signal Processing for Chemical Sensing. Studies in Computational Intelligence, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00176-5_5
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DOI: https://doi.org/10.1007/978-3-642-00176-5_5
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