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Catechol determination in compost bioremediation using a laccase sensor and artificial neural networks

Catechol determination in compost bioremediation using a laccase sensor and artificial neural... An electrochemical biosensor based on the immobilization of laccase on magnetic core-shell (Fe3O4–SiO2) nanoparticles was combined with artificial neural networks (ANNs) for the determination of catechol concentration in compost bioremediation of municipal solid waste. The immobilization matrix provided a good microenvironment for retaining laccase bioactivity, and the combination with ANNs offered a good chemometric tool for data analysis in respect to the dynamic, nonlinear, and uncertain characteristics of the complex composting system. Catechol concentrations in compost samples were determined by using both the laccase sensor and HPLC for calibration. The detection range varied from 7.5 × 10–7 to 4.4 × 10–4 M, and the amperometric response current reached 95% of the steady-state current within about 70 s. The performance of the ANN model was compared with the linear regression model in respect to simulation accuracy, adaptability to uncertainty, etc. All the results showed that the combination of amperometric enzyme sensor and artificial neural networks was a rapid, sensitive, and robust method in the quantitative study of the composting system. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Analytical and Bioanalytical Chemistry Springer Journals

Catechol determination in compost bioremediation using a laccase sensor and artificial neural networks

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References (28)

Publisher
Springer Journals
Copyright
Copyright © 2008 by Springer-Verlag
Subject
Chemistry; Ecotoxicology; Physical Chemistry ; Inorganic Chemistry ; Food Science ; Analytical Chemistry
ISSN
1618-2642
eISSN
1618-2650
DOI
10.1007/s00216-008-2049-1
pmid
18398603
Publisher site
See Article on Publisher Site

Abstract

An electrochemical biosensor based on the immobilization of laccase on magnetic core-shell (Fe3O4–SiO2) nanoparticles was combined with artificial neural networks (ANNs) for the determination of catechol concentration in compost bioremediation of municipal solid waste. The immobilization matrix provided a good microenvironment for retaining laccase bioactivity, and the combination with ANNs offered a good chemometric tool for data analysis in respect to the dynamic, nonlinear, and uncertain characteristics of the complex composting system. Catechol concentrations in compost samples were determined by using both the laccase sensor and HPLC for calibration. The detection range varied from 7.5 × 10–7 to 4.4 × 10–4 M, and the amperometric response current reached 95% of the steady-state current within about 70 s. The performance of the ANN model was compared with the linear regression model in respect to simulation accuracy, adaptability to uncertainty, etc. All the results showed that the combination of amperometric enzyme sensor and artificial neural networks was a rapid, sensitive, and robust method in the quantitative study of the composting system.

Journal

Analytical and Bioanalytical ChemistrySpringer Journals

Published: Apr 9, 2008

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