Electrochemical inhibition bacterial sensor array for detection of water pollutants: artificial neural network (ANN) approach

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ABU-ALI, Hisham, NABOK, Aleksey and SMITH, Thomas (2019). Electrochemical inhibition bacterial sensor array for detection of water pollutants: artificial neural network (ANN) approach. Analytical and Bioanalytical Chemistry.

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Official URL: https://link.springer.com/article/10.1007/s00216-0...
Open Access URL: https://link.springer.com/content/pdf/10.1007%2Fs0... (Published version)
Link to published version:: https://doi.org/10.1007/s00216-019-01853-8

Abstract

This work reports on further development of an inhibition electrochemical sensor array based on immobilized bacteria for the preliminary detection of a wide range of organic and inorganic pollutants, such as heavy metal salts (HgCl2, PbCl2, CdCl2), pesticides (atrazine, simazine, DDVP), and petrochemicals (hexane, octane, pentane, toluene, pyrene, and ethanol) in water. A series of DC and AC electrochemical measurements, e.g., cyclic voltammograms and impedance spectroscopy, were carried out on screen-printed gold electrodes with three types of bacteria, namely Escherichia coli, Shewanella oneidensis, and Methylococcus capsulatus, immobilized via poly L-lysine. The results obtained showed a possibility of pattern recognition of the above pollutants by their inhibition effect on the three bacteria used. The analysis of a large amount of experimental data was carried out using an artificial neural network (ANN) programme for more accurate identification of pollutants as well as the estimation of their concentration. The results are encouraging for the development of a simple and cost-effective biosensing technology for preliminary in-field analysis (screening) of water samples for the presence of environmental pollutants.

Item Type: Article
Uncontrolled Keywords: Artificial neural network (ANN); Electrochemical sensor array; Immobilized bacteria; Inhibition biosensor; Pattern recognition; Water pollution; 03 Chemical Sciences; 06 Biological Sciences; 09 Engineering; Analytical Chemistry
Identification Number: https://doi.org/10.1007/s00216-019-01853-8
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 10 Jun 2019 15:37
Last Modified: 18 Mar 2021 05:10
URI: https://shura.shu.ac.uk/id/eprint/24703

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