Simultaneous determination of hydroquinone and catechol in compost bioremediation using a tyrosinase biosensor and artificial neural networks

Abstract

A biosensor based on tyrosinase immobilized with ordered mesoporous carbon–Au (OMC–Au), L-lysine membrane and Au nanoparticles (tyrosinase/OMC–Au/L-lysine/Au) was combined with artificial neural networks (ANNs) for the simultaneous determination of catechol (CC) and hydroquinone (HQ) in compost bioremediation of municipal solid waste. The good performance of biosensor provided the potential applicability for the simultaneous identification and quantification of catechol and hydroquinone in real samples, 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. Good prediction ability was attained after the ANNs model optimization, and the direct detection range for catechol and hydroquinone were directly analyzed by the ANNs model and varied between 1.0 × 10⁻⁷ and 1.1 × 10⁻⁴ M, significantly extended compared to the linear model (4.0 × 10⁻⁷ to 8.0 × 10⁻⁵ M). Finally, the performance of the ANNs model was compared with the linear regression model. The results demonstrate that the prediction results by the ANNs model are more precise than those by the linear regression, and the latter was far from accurate at high levels of catechol and hydroquinone beyond the linear range. All the results show that the combination of the biosensor and ANNs is a rapid and sensitive method in the quantitative study of composting system.