The human olfactory system can classify new odors in a dynamic environment
with varying odor complexity and concentration, while simultaneously reducing the
influence of stable background odors. Replication of this capability has remained an
active area of research over the past 3 decades and has great potential to advance medical
diagnostics, environmental monitoring and industrial monitoring, among others. New
methods for rapid dynamic temporal evaluation of chemical sensor arrays for the
monitoring of analytes is explored in this work. One such method is high and low bandpass
filtering of changing sensor responses; this is applied to reduce the effects of
background noise and sensor drift over time. Processed sensor array responses, coupled
with principal component analysis (PCA), will be used to develop a novel approach to
classify odors in the presence of changing sensor responses associated with evolving odor
concentrations. These methods will enable the removal of noise and drift, as well as
facilitating the normalization to decouple classification patterns from intensity; lastly,
PCA and artificial neural networks (ANNs) will be used to demonstrate the capability of
this approach to function under dynamic conditions, where concentration is changing
temporally. / February 2016
| Identifer | oai:union.ndltd.org:MANITOBA/oai:mspace.lib.umanitoba.ca:1993/31056 |
| Date | 13 January 2016 |
| Creators | Ryman, Shaun K. |
| Contributors | Freund, Michael (Chemistry), Perreault, Hélène (Chemistry) Bruce, Neil (Computer Science) |
| Source Sets | University of Manitoba Canada |
| Detected Language | English |
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