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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Publicado en:BioMed Research International Vol. 2013; pp. 813519 - 813520
Autores principales: Ferentinos, Konstantinos P, Yialouris, Costas P, Blouchos, Petros, Moschopoulou, Georgia, Kintzios, Spyridon
Formato: Journal Article
Publicado: Wiley-Blackwell 2013
Acceso en línea:Ver este registro en EBSCOhost
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      pub: Wiley-Blackwell
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        atl: Pesticide residue screening using a novel artificial neural network combined with a bioelectric cellular biosensor.
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          Ferentinos, Konstantinos P
          Yialouris, Costas P
          Blouchos, Petros
          Moschopoulou, Georgia
          Kintzios, Spyridon
        affil: Laboratory of Informatics, School of Food Science, Biotechnology and Development, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
      sug:
        subj:
          Biosensing Techniques Equipment and Supplies
          Biosensing Techniques Methods
          Electricity
          Neural Networks (Computer)
          Pesticides Analysis
          Animals
          Acids, Acyclic Analysis
          Cell Line
          Mice
          Organophosphorus Compounds Analysis
          Insecticides Analysis
      ab: 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.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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