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Computational analysis of olfaction and artificial nose technologies

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TitleInfo
Title
Computational analysis of olfaction and artificial nose technologies
Name (type = personal)
NamePart (type = family)
Tsitron
NamePart (type = given)
Julia
NamePart (type = date)
1976-
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Julia Tsitron
Role
RoleTerm (authority = RULIB)
author
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Morozov
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Alexandre V.
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Alexandre V. Morozov
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Advisory Committee
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chair
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Bhanot
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Gyan
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Gyan Bhanot
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Advisory Committee
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internal member
Name (type = personal)
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Sontag
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Eduardo
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Eduardo Sontag
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Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Chen
NamePart (type = given)
Kevin
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Kevin Chen
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Broach
NamePart (type = given)
James R.
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James R. Broach
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2013
DateOther (qualifier = exact); (type = degree)
2013-10
Place
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xx
Language
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eng
Abstract (type = abstract)
Cross-selective receptor arrays coupled with higher-order neural processing can be seen in naturally occurring olfactory systems, where a large number and variety of analytes can be detected, distinguished, even quantified using relatively few receptors and clever combinatorial odor decoding. This strategy has been imitated in artificial sensor array systems that are paired with computational signal-processing tools in diverse applications that range from vintage wine year discrimination to disease diagnosis. However, the complexity of receptor response patterns to even a single analyte, coupled with non-linearity of response to mixtures of analytes, makes quantitative inference of individual compound concentrations within mixtures a challenging task. In this work, I show how output from two distinct types of sensor arrays, each combined with Bayesian analysis, can be used to predict component concentrations in complex mixtures. In the first case, the array consists of four engineered G-protein-coupled receptors used for deciphering mixtures of highly related sugar nucleotides. We employ a biophysical model that explicitly takes receptor-ligand interactions into account in order to quantify mixture constituents. Furthermore, we develop a universal metric of receptor array performance, and use it to study the fundamental limits imposed on the accuracy of ligand recognition by the physics and biology of receptor-ligand interactions. This provides design guidelines for sensor arrays optimized for mixture analysis. Antagonistic receptor response, well-known to play an important role in biological systems, proves to be essential for precise recognition of mixture components. The second array consists of a mixed-potential electrochemical sensor operating under different applied bias currents to monitor gas mixtures in diesel engine exhaust. Here, the sensitivity and selectivity of a device is tuned by current application, thus a single sensor serves as an entire array when operated under multiple conditions. Both a linear and non-linear model are used to quantify ammonia gas in the presence of propylene interference. While more data-intensive, the nonlinear model captures cross-interference between analytes and yields more accurate predictions. Our Bayesian methodology is easily generalized to other `artificial nose' applications by the inclusion of additional models.
Subject (authority = RUETD)
Topic
Computational Biology and Molecular Biophysics
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
Identifier
ETD_5114
PhysicalDescription
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electronic resource
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application/pdf
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text/xml
Note
Supplementary File: Dataset.xls
Extent
xii, 88, [7] p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = vita)
Includes vita
Note (type = statement of responsibility)
by Julia Tsitron
Subject (authority = ETD-LCSH)
Topic
Olfactory sensors
Subject (authority = ETD-LCSH)
Topic
Bayesian statistical decision theory
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T32805PH
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Tsitron
GivenName
Julia
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2013-10-01 11:53:16
AssociatedEntity
Name
Julia Tsitron
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
RightsEvent
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2013-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2014-05-02
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 2nd, 2014.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

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windows xp
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ETD
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