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 x 10(-7) to 4.4 x 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