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

Area of Science:

• Environmental Science
• Analytical Chemistry
• Biotechnology

Background:

• Municipal solid waste (MSW) bioremediation requires accurate monitoring of key compounds like catechol.
• Traditional analytical methods may struggle with the complex, dynamic nature of composting systems.
• Enzyme-based biosensors offer potential for sensitive and selective detection.

Purpose of the Study:

• To develop and validate an electrochemical biosensor for catechol determination in MSW compost.
• To integrate artificial neural networks (ANNs) for enhanced data analysis of the complex composting system.
• To evaluate the performance of the developed biosensor system compared to conventional methods.

Main Methods:

• Immobilization of laccase enzyme onto magnetic core-shell (Fe(3)O(4)-SiO(2)) nanoparticles.
• Development of an amperometric electrochemical biosensor.
• Application of artificial neural networks (ANNs) for chemometric analysis and data interpretation.
• Calibration and validation using High-Performance Liquid Chromatography (HPLC).

Main Results:

• The laccase-nanoparticle immobilization matrix maintained enzyme bioactivity.
• The combined biosensor and ANNs system demonstrated a wide detection range (7.5 × 10⁻⁷ to 4.4 × 10⁻⁴ M) for catechol.
• The biosensor achieved a rapid response time (95% steady-state current in ~70 s).
• ANN model showed superior performance in simulation accuracy and adaptability to uncertainty compared to linear regression.

Conclusions:

• The developed amperometric enzyme biosensor coupled with ANNs is a rapid, sensitive, and robust tool for quantitative analysis in composting systems.
• This integrated approach effectively addresses the dynamic and nonlinear characteristics of bioremediation monitoring.
• The method provides a reliable alternative for assessing catechol concentrations during MSW composting.