NDLI: Odor discrimination using a hybrid-device olfactory biosensor

Content Provider	IEEE Xplore Digital Library
Author	Hetling, J.R. Myrick, A.J. Park, K.-C. Baker, T.C.
Copyright Year	2003
Description	Author affiliation: Dept. of Bioeng., Chicago Univ., IL, USA (Hetling, J.R.; Myrick, A.J.)
Abstract	Current trends in artificial nose research are strongly motivated by knowledge of biological olfactory systems, but are primarily confined to improving pattern recognition strategies for data derived from a relatively simple sensor array. Biological olfactory systems are able to discriminate weak, transient, broad-band signals ranging over a poorly-defined parameter space, and therefore outperform current artificial nose systems in several respects. A biological olfactory sense organ, the insect antenna, has been exploited in a hybrid-device biosensor. An algorithm was developed to analyze the electrophysiological responses recorded from a sensor array comprised of antennae from different species of insects. A training period during which the array was exposed to known target odors established response signatures for those odors. Subsequent odor stimuli were then classified using a forced-choice nearest neighbor technique. As odorants arrived in discrete packets in the turbulent air stream, individual sensor response events lasted less than one second, and could be classified with accuracy dependant on the differential tuning of the sensor array to the compounds being classified.
Starting Page	146
Ending Page	149
File Size	457313
Page Count	4
File Format	PDF
ISBN	0780375793
DOI	10.1109/CNE.2003.1196778
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-03-20
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Insects Olfactory Nose Sensor systems Sense organs Pattern recognition Biosensors Sensor arrays Antenna arrays
Content Type	Text
Resource Type	Article

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