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BioMed Research International
Volume 2013 (2013), Article ID 813519, 8 pages
http://dx.doi.org/10.1155/2013/813519
Research Article

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

1Laboratory of Informatics, School of Food Science, Biotechnology and Development, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
2Laboratory of Enzyme Technology, School of Food Science, Biotechnology and Development, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece

Received 9 April 2013; Revised 12 June 2013; Accepted 3 July 2013

Academic Editor: Eldon R. Rene

Copyright © 2013 Konstantinos P. Ferentinos et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.