ArticleBioMed research international2013
Pesticide residue screening using a novel artificial neural network combined with a bioelectric cellular biosensor.
Article in BioMed research international, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 21 citations in OpenAlex.
- Application of Chemometrics in Biosensing: A Review.Biosensors · 2020Review
- Bioelectric Sensors: On the Road for the 4.0 Diagnostics and Biomedtech Revolution.Biosensors · 2020Article
- Application of Multivariate Statistical Analysis to Simultaneous Spectrophotometric Enzymatic Determination of Glucose and Cholesterol in Serum Samples.International journal of analytical chemistry · 2019Article
- The Application of Classification and Regression Trees for the Triage of Women for Referral to Colposcopy and the Estimation of Risk for Cervical Intraepithelial Neoplasia: A Study Based on 1625 Cases with Incomplete Data from Molecular Tests.BioMed research international · 2015Article
- Safety issues and new rapid detection methods in traditional Chinese medicinal materials.Acta pharmaceutica Sinica. B · 2015Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.