Pesticide Residue Screening Using a Novel Artificial Neural Network Combined with a Bioelectric Cellular Biosensor

We developed a novel artificial neural network (ANN) system able to detect and classify pesticide residues. The novel ANN is coupled, in a customized way, to a cellular biosensor operation based on the bioelectric recognition assay (BERA) and able to simultaneously assay eight samples in three minut...

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Published inBioMed research international Vol. 2013; no. 2013; pp. 1 - 8
Main Authors Moschopoulou, Georgia, Blouchos, Petros, Yialouris, Constantinos P., Ferentinos, Konstantinos P., Kintzios, Spyridon
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2013
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Abstract We developed a novel artificial neural network (ANN) system able to detect and classify pesticide residues. The novel ANN is coupled, in a customized way, to a cellular biosensor operation based on the bioelectric recognition assay (BERA) and able to simultaneously assay eight samples in three minutes. The novel system was developed using the data (time series) of the electrophysiological responses of three different cultured cell lines against three different pesticide groups (carbamates, pyrethroids, and organophosphates). Using the novel system, we were able to classify correctly the presence of the investigated pesticide groups with an overall success rate of 83.6%. Considering that only 70,000–80,000 samples are annually tested in Europe with current conventional technologies (an extremely minor fraction of the actual screening needs), the system reported in the present study could contribute to a screening system milestone for the future landscape in food safety control.
AbstractList We developed a novel artificial neural network (ANN) system able to detect and classify pesticide residues. The novel ANN is coupled, in a customized way, to a cellular biosensor operation based on the bioelectric recognition assay (BERA) and able to simultaneously assay eight samples in three minutes. The novel system was developed using the data (time series) of the electrophysiological responses of three different cultured cell lines against three different pesticide groups (carbamates, pyrethroids, and organophosphates). Using the novel system, we were able to classify correctly the presence of the investigated pesticide groups with an overall success rate of 83.6%. Considering that only 70,000-80,000 samples are annually tested in Europe with current conventional technologies (an extremely minor fraction of the actual screening needs), the system reported in the present study could contribute to a screening system milestone for the future landscape in food safety control.
Author Blouchos, Petros
Yialouris, Constantinos P.
Kintzios, Spyridon
Moschopoulou, Georgia
Ferentinos, Konstantinos P.
AuthorAffiliation 1 Laboratory of Informatics, School of Food Science, Biotechnology and Development, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
2 Laboratory of Enzyme Technology, School of Food Science, Biotechnology and Development, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
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Snippet We developed a novel artificial neural network (ANN) system able to detect and classify pesticide residues. The novel ANN is coupled, in a customized way, to a...
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SubjectTerms Animals
Biosensing Techniques - instrumentation
Biosensing Techniques - methods
Carbamates - analysis
Cell Line
Electricity
Humans
Mice
Neural Networks (Computer)
Organophosphates - analysis
Pesticide Residues - analysis
Pyrethrins - analysis
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Title Pesticide Residue Screening Using a Novel Artificial Neural Network Combined with a Bioelectric Cellular Biosensor
URI https://search.emarefa.net/detail/BIM-1030951
https://dx.doi.org/10.1155/2013/813519
https://www.ncbi.nlm.nih.gov/pubmed/23984408
https://search.proquest.com/docview/1443371877
https://pubmed.ncbi.nlm.nih.gov/PMC3745894
Volume 2013
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