Optimization of fermentation medium components by response surface methodology (RSM) and artificial neural network hybrid with genetic algorithm (ANN-GA) for lipase production by Burkholderia cenocepacia ST8 using used automotive engine oil as substrate - Lau, Hui-Lane; Faizal Wong, Fadzlie Wong; Raja Abd Rahman, Raja Noor Zaliha; Mohamed, Mohd Shamzi; Ariff, Arbakariya; Hii, Siew-Ling (Elsevier, 2023)
Bioconversion of used automotive engine oil (UEO) into lipase was conducted via submerged fermentation by Burkholderia cenocepacia ST8, as a strategy for value-added product generation and waste management. Response surface methodology (RSM) and artificial neural network hybrid with genetic algorithm (ANN-GA) were employed to optimize the fermentation medium for enhancing extracellular lipase production by B. cenocepacia ST8. Employing a four-factor-five-level central composite rotatable experimental design (CCRD), a reduced quartic polynomial RSM model and ANN model (4-4-1) trained using Bayesian Regularization were developed to attain the optimized fermentation medium for maximum level of lipase production. The RSM model predicted the following as the optimum media constituents: 2.28 v/v of Tween 80, 2.26 v/v of UEO, 0.79 w/v of nutrient broth, and 1.33 w/v of gum arabic, with an actual lipase yield of 216 U/mL. While, ANN-GA predicted the optimum media constituents to be 3 v/v of Tween 80, 3 v/v of UEO, 0.72 w/v of nutrient broth, and 3.38 w/v of gum arabic, with actual lipase yield of 225 U/mL. In comparison to the unoptimized medium, optimized RSM and ANN-GA systems both demonstrated a 1.6-fold increment in lipase production. Tween 80 and nutrient broth concentrations were the most important variables influencing the lipase production. The findings of this study indicated that the ANN-GA and RSM could be useful for effective optimization of the fermentation medium for enzyme production.