Modified screen printed electrodes for electrochemical sensing applications in food and beverage analysis

dc.check.embargoformatNot applicableen
dc.check.infoNo embargo requireden
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dc.contributor.advisorMoore, Ericen
dc.contributor.authorDuffy, Gerard
dc.contributor.funderINSPIREen
dc.date.accessioned2017-09-04T10:16:42Z
dc.date.available2017-09-04T10:16:42Z
dc.date.issued2017
dc.date.submitted2017
dc.description.abstractElectrochemical sensor technology has the potential to revolutinise analytical measurements in food analysis by providing a cheap, easy to use and portable alternative to traditional chromotography and mass spectrometry based techniques. In this thesis two such sensors are developed - one for the detection of caffeine and other for the detection of the mycotoxin, deoxynivalenol. These sensors are developed from both a food safety and quality control point of view. The majority of sensor development has been for the medical point of care sector, however apart from blood glucose sensing, commerical successes have been limited. Regulatory barriers are not as significant in the food industry compared to the medical industry and hence, real world applications should be more feasible. Chapter 1 provides a general introduction to sensor technology for food analysis with an emphasis on electrochemical transducers and modified electrodes in particular. Background theory is outlined for both fundamental electrochemistry and immunoassay development and previously published work in electrochemical sensors for food analysis is summarised. In Chapter 2 a number of electrochemical pretreatment techniques are compared with multiwall carbon nanotubes (MWCNT) and reduced graphene oxide (RGO) modified screen printed electrodes in the development of an electrochemical immunosensor for mouse IgG detection. It was found that a MWCNT/Nafion modified electrode had the most promising analytical characteristics with an LOD of 0.2 ppm, IC50 value of 0.3 ppm, linear range of 0.04 - 2.7 ppm and R 2 value of 0.9286. Subsequent to the optimisation of an electrode material for this model analyte (mouse IgG), an electrochemical immunosensor for deoxynivalenol was developed in Chapter 3. Firstly, an ELISA protocol was developed with a LOD of 0.534 ppm and linear range of 0.9 - 53 ppm, followed by the transfer of this protocol onto the MWCNT/Nafion electrode. A limit of detection of 0.95 ppm and IC50 value of 2 ppm were found. These values are promising considering that the EU maximum residue limit (MRL) for DON in wheat samples is 1.75 ppm. However, the developed sensor was unable to detect DON in wheat samples suggesting that a more sophisticated sample pretreatment method would need to be investigated. In Chapter 4, a number of different approaches for electrochemical caffeine detection are investigated namely electrochemically pretreated, nafion modified and screen printed graphene electrodes. It was found that the nafion modified electrode was the most suitable in energy drinks samples. This sensor had a LOD of 8 µM and linear range of 10 - 128 µM. Finally, in Chapter 5 some suggestions are given to future directions for electrochemical sensor development.en
dc.description.sponsorshipHigher Education Authority (Integrated Nanoscience Platform for Ireland (INSPIRE))en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationDuffy, G. 2017. Modified screen printed electrodes for electrochemical sensing applications in food and beverage analysis. PhD Thesis, University College Cork.en
dc.identifier.endpage220en
dc.identifier.urihttps://hdl.handle.net/10468/4629
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2017, Gerard Duffy.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectElectrochemistryen
dc.subjectBiosensoren
dc.subjectFooden
dc.subjectNanotechnologyen
dc.thesis.opt-outfalse
dc.titleModified screen printed electrodes for electrochemical sensing applications in food and beverage analysisen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD (Science)en
ucc.workflow.supervisore.moore@ucc.ie
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