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

Abstract

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 R2 and MSE for BBD were found to be 0.98, 9.88, while for ANN were 0.97, 38.25, respectively. Based on higher R2 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.