Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.677732
Title: Towards building a robot maggot nose
Author: Pickford, Christopher
ISNI:       0000 0004 5369 3170
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2014
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Abstract:
Artificial olfaction has the potential to revolutionise medical diagnosis, speed up threat detection, and provide long-term data collection on atmospheric pollution. Current technology provides a means to detect some compounds, but detection speed is slow, and accuracy is often balanced against cost and reusability of devices. Insects have long been the inspiration for artificial olfaction and are able to detect low concentrations of compounds over vast areas and navigate towards these targets. A deeper understanding of how this is achieved could inform the design of better artificial olfactory devices. What features of the insect olfactory system allow the rapid detection of miniscule concentrations of a wide variety of compounds? How does an insect discriminate odours using a limited number of olfactory receptor types?Using UAS/GAL4 technology, Drosophila melanogaster larvae expressing only a single functional olfactory sensory neuron (OSN), of their full repertoire of 21, were used to explore peripheral olfactory responses. Three different fly lines, which each expressed different olfactory receptor (OR) types, were used to record the electrophysiological responses of the peripheral OSNs to a panel of biologically significant odours, at differing concentrations. These responses were compared to those from a leading electronic nose (metal oxide sensors) for the same odour conditions, and the features of the responses were characterised. A novel odour delivery system was also developed to accurately deliver these odours and concentrations repeatably, and was validated using a commercially available photoionisation detector. The ability to correctly select the odour from which a response was recorded, out of a choice of five odours, at two concentrations each, was scrutinised using a classifier algorithm. The three OSN types achieved 53%, 62% and 75% accuracy, respectively. This is the first instance that Drosophila larvae have been conclusively shown to be able to discriminate odour concentrations using only a single peripheral OSN. The two metal oxide sensors achieved 92% and 95% accuracy under the same conditions. Whilst the features of the responses used to discriminate odours differed between the biological and electronic systems, the time frame required for correct classification of sensor data was only, in some cases, three seconds longer than in OSNs (~0.5 seconds), indicating that metal oxide sensors may have a useful role to play in biologically inspired artificial olfaction.
Supervisor: Not available Sponsor: BBSRC
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.677732  DOI: Not available
Keywords: Olfaction ; Robotics ; classification ; Drosophila ; enose ; MATLAB ; Neuron ; Electronic
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