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

• Published in: BioMed 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
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2013
• Subjects: Animals; Biosensing Techniques - instrumentation; Biosensing Techniques - methods; Carbamates - analysis; Cell Line; Electricity; Humans; Mice; Neural Networks (Computer); Organophosphates - analysis; Pesticide Residues - analysis; Pyrethrins - analysis
• ISSN: 2314-6133; 2314-6141
• DOI: 10.1155/2013/813519

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.