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The use of artificial neural networks as a component of a cell-based biosensor device for the detection of pesticides

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dc.contributor.author Ferentinos, KP en
dc.contributor.author Yialouris, CP en
dc.contributor.author Blouchos, P en
dc.contributor.author Moschopoulou, G en
dc.contributor.author Tsourou, V en
dc.contributor.author Kintzios, S en
dc.date.accessioned 2014-06-06T06:52:10Z
dc.date.available 2014-06-06T06:52:10Z
dc.date.issued 2012 en
dc.identifier.issn 18777058 en
dc.identifier.uri http://dx.doi.org/10.1016/j.proeng.2012.09.313 en
dc.identifier.uri http://62.217.125.90/xmlui/handle/123456789/5878
dc.subject Artificial neural networks en
dc.subject Cell-based biosensor en
dc.subject Pesticides en
dc.title The use of artificial neural networks as a component of a cell-based biosensor device for the detection of pesticides en
heal.type conferenceItem en
heal.identifier.primary 10.1016/j.proeng.2012.09.313 en
heal.publicationDate 2012 en
heal.abstract The present study describes an artificial neural network (ANN) system that uses a cell-based biosensor based on the Bioelectric Recognition Assay (BERA) methodology, for the detection and classification of pesticide residues in food commodities. The insecticidal compounds carbaryl and chlorpyrifos as well as the pyrethroid group were used as models for the training of the ANN. The biosensor was based on neuroblastoma N2a cells, which are targets of the pesticides due to the inhibition of the enzyme acetylcholine esterase by them. The response of the biosensor to different concentrations (samples) of either pesticide was recorded as a time-series of potentiometric measurements (in Volts). The feedforward methodology was used for the development of the ANN, which was trained with the backpropagation training algorithm. The results of the application of the developed system indicate that the novel classification methodology exhibits promising performance as a central component of a rapid, high throughput screening system for pesticide residues. © 2012 The Authors. Published by Elsevier Ltd. en
heal.journalName Procedia Engineering en
dc.identifier.volume 47 en
dc.identifier.doi 10.1016/j.proeng.2012.09.313 en
dc.identifier.spage 989 en
dc.identifier.epage 992 en


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