[Amperometric determination of hydroquinone in compost system using artificial neural networks]

To resolve the problems of small detection concentration scope and poor precision in amperometric determination of hydroquinone in compost system by laccase biosensor, an artificial neural network (ANN) model was established to analyze the current signal and predict hydroquinone concentration. The response currents and hydroquinone concentration were taken as input variables and output variable respectively. The optimal configuration of networks was obtained: hidden neuron number, 7; input neuron number, 5; transfer function, Tansig-logsig; algorithm, Levenberg-Marquardt. The prediction performances of ANN were compared with nonlinear regression model (NR). The RMSE (root mean square of error) and correlation coefficient between calculated values and measured values of NR are 14.4419 micromol/L and 0.9944 respectively, while those of ANN reach 4.4442 micromol/L and 0.9996, respectively, showing better prediction performances than NR. Meanwhile, the hydroquinone detection scope of 0.015-450 micromol/L is achieved. Both the detection scope and precision of hydroquinone in compost system were further improved on the present level.