Optimization of laccase enzyme extraction from spent mushroom waste of Pleurotus florida through ANN-PSO modeling: An ecofriendly and economical approach

Environ Res. 2023 Apr 1:222:115345. doi: 10.1016/j.envres.2023.115345. Epub 2023 Jan 25.


The cardinal focus of this study is to optimize the best reaction conditions for maximizing laccase activity from spent mushroom waste (SMW) of Pleurotus florida. Optimization process parameters were studied by the modeling techniques, artificial neural networking (ANN) embedded in particle swarm optimization (PSO), and response surface model (RSM). The best topology of ANN-PSO architecture was obtained on 4-10-1. The R2, IOA, MSE, and MAE values of the ANN model were obtained as 0.98785, 0.9939, 0.0023, and 0.0251 while, that of the RSM model were obtained as 0.74290, 0.9210, 0.0244, and 0.1110 respectively. The higher values of R2, IOA, and lower values of MSE and MAE of the ANN-PSO model depict that ANN-PSO outperformed compared to RSM and also verified the effectiveness of the ANN-PSO model. The ANN-PSO model performance demonstrates the robustness of the technique in optimizing laccase activity in SMW of P. florida. The optimization results revealed that pH 4.5, time 3 h, solid: solution ratio 1:5, and ABTS concentration of 1 mM was optimal for achieving maximum laccase activity at temperature 30 °C. The enzymatic activity of crude laccase enzyme was obtained as 1.185 U ml-1 without loss of enzyme activity. Additionally, crude laccase enzyme was 1.74 fold partially purified, and 83.54% of the enzyme was yielded. Out of all the independent process variables, ABTS and pH had an influence on laccase activity. Therefore, we anticipate that the findings of this investigation will reduce the ambiguity in maximizing laccase activity and ease the screening process. This study also highlights the comparative cost evaluation of crude laccase enzyme extracted from P. florida and commercial enzymes. There is a great potential for the utilization of the laccase enzyme extracted from SMW and using it for the degradation of recalcitrant micropollutants. Thus, SMW promises a cost-effective and sustainable approach leading towards circular economy.

Keywords: Artificial neural networking (ANN); Circular economy; Laccase; Particle swarm optimization (PSO); Response surface analysis; Spent mushroom waste (SMW).

MeSH terms

  • Agaricales*
  • Laccase
  • Neural Networks, Computer
  • Pleurotus*


  • Laccase
  • 2,2'-azino-di-(3-ethylbenzothiazoline)-6-sulfonic acid