A screen-printed, amperometric biosensor array incorporated into a novel automated system for the simultaneous determination of organophosphate pesticides

Organophosphate pesticides present serious risks to human and environmental health. A rapid reliable, economical and portable analytical system will be of great benefit in the detection and prevention of contamination. A biosensor array based on six acetylcholinesterase enzymes for use in a novel au...

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Bibliographic Details
Published in:Biosensors & bioelectronics 2011-02, Vol.26 (6), p.2847-2851
Main Authors: Crew, A., Lonsdale, D., Byrd, N., Pittson, R., Hart, J.P.
Format: Article
Language:English
Subjects:
Acetylcholinesterase
Arrays
Automated
Automation
Biological and medical sciences
Biosensing Techniques - instrumentation
Biosensing Techniques - methods
Biosensing Techniques - statistics & numerical data
Biosensor
Biosensors
Biotechnology
Chronoamperometry
Electrochemical Techniques
Enzymes, Immobilized
Equipment Design
Food Contamination - analysis
Foods
Fundamental and applied biological sciences. Psychology
Humans
Mathematical analysis
Methods. Procedures. Technologies
Neural networks
Neural Networks (Computer)
Organophosphate
Organophosphates
Organophosphates - analysis
Pesticides
Pesticides - analysis
Screen-printed carbon electrode
Various methods and equipments
Water Pollutants, Chemical - analysis
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Summary:Organophosphate pesticides present serious risks to human and environmental health. A rapid reliable, economical and portable analytical system will be of great benefit in the detection and prevention of contamination. A biosensor array based on six acetylcholinesterase enzymes for use in a novel automated instrument incorporating a neural network program is described. Electrochemical analysis was carried out using chronoamperometry and the measurement was taken 10s after applying a potential of 0V vs. Ag/AgCl. The total analysis time for the complete assay was less than 6min. The array was used to produce calibration data with six organophosphate pesticides (OPs) in the concentration range of 10−5 M to 10−9 M to train a neural network. The output of the neural network was subsequently evaluated using different sample matrices. There were no detrimental matrix effects observed from water, phosphate buffer, food or vegetable extracts. Furthermore, the sensor system was not detrimentally affected by the contents of water samples taken from each stage of the water treatment process. The biosensor system successfully identified and quantified all samples where an OP was present in water, food and vegetable extracts containing different OPs. There were no false positives or false negatives observed during the evaluation of the analytical system. The biosensor arrays and automated instrument were evaluated in situ in field experiments where the instrument was successfully applied to the analysis of a range of environmental samples. It is envisaged that the analytical system could provide a rapid detection system for the early warning of contamination in water and food.
ISSN:0956-5663
1873-4235
DOI:10.1016/j.bios.2010.11.018