Optimized degradation of bisphenol A by immobilized laccase from Trametes versicolor using Box-Behnken design (BBD) and artificial neural network (ANN)

This study represents one of the first attempts towards bisphenol A (BPA) degradation by Trametes versicolor laccase immobilized on Ba-alginate beads. The effect of input variables including temperature (20–40 °C), BPA concentration (2–10 mg/L) and time (10–90 min) were studied using Box-Behnken design (BBD) and artificial neural network (ANN). The maximum BPA degradation of 84.34% was obtained when the temperature was 40 °C, BPA concentration 2 mg/L and time 50 min, which was accurately predicted by BBD (83.48%) and ANN (84.33%), proving the accuracy of prediction for both models. The values of R 2 and MSE for BBD were found to be 0.98, 9.88, while for ANN were 0.97, 38.25, respectively. Based on higher R 2 value and lower MSE, BBD was slightly better than ANN. The immobilized laccase showed higher storage stability than free laccase by retaining 68.64% and 44.62% of their activity at the same experimental conditions. Furthermore, BPA transformation was confirmed by Fourier-transform infrared spectroscopy (FTIR) analysis. GC-MS had detected the oxidative degradation products from BPA. Results showed that Ba-alginate immobilized laccase could be a promising biocatalyst in treating organic pollutants .