Application of statistical modeling for the production of highly pure rhamnolipids using magnetic biocatalysts: Evaluating its efficiency as a bioremediation agent.

In the present study, highly pure rhamnolipids (RLs) was produced using biocatalysts immobilized on amino-functionalized chitosan coated magnetic nanoparticles. Upon immobilizing naringinase and Candida antarctica lipase B (CaLB) under the optimized conditions, an enhanced operational stability with biocatalytic loads of 935 ± 2.4 U/g (naringinase) and 825 ± 4.1 U/g (CaLB) were achieved. Subsequently, the immobilized biocatalysts were utilized sequentially in a two-step RLs synthesis process. The key parameters involved in RLs production were optimized using artificial neural network (ANN) coupled genetic algorithm (GA) and were compared with composite central design (CCD). On validating the efficiency of both models, mean square errors of 1.58% (CCD) and 1.04% (ANN) were obtained. Optimization of parameters by ANN-GA resulted in 1.2-fold increase in experimental RLs yield (80.53%), which was 1.05-fold higher when compared to CCD model. Further, to establish the efficiency of RLs as a bioremediation agent, it was utilized as washing agent. It was observed that at a soil to RLs volume of 1:05, RLs concentration of 0.4 mg/mL, a 95.35 ± 1.33% removal of Total Petroleum Hydrocarbons (TPHs) was obtained at 35 ℃ and 160 rpm in 75 min. Thus, this strategy provides an efficient biocatalytic toolbox for RLs synthesis, which can be effectively used as a bioremediation agent.