Sanjay Nene

Use of response surface optimization for the production of biosurfactant from Rhodococcus spp. MTCC 2574

The production of biosurfactant from Rhodococcus spp. MTCC 2574 was effectively enhanced by response surface methodology (RSM). Rhodococcus spp. MTCC 2574 was selected through screening of seven different Rhodococcus strains. The preliminary screening experiments (one-factor at a time) suggested that carbon source: mannitol, nitrogen source: yeast extract and meat peptone and inducer: n-hexadecane are the critical medium components. The concentrations of these four media components were optimized by using central composite rotatable design (CCRD) of RSM. The adequately high R2 value (0.947) and F score 19.11 indicated the statistical significance of the model. The optimum medium composition for biosurfactant production was found to contain mannitol (1.6 g/L), yeast extract (6.92 g/L), meat peptone (19.65 g/L), n-hexadecane (63.8 g/L). The crude biosurfactant was obtained from methyl tert-butyl ether extraction. The yield of biosurfactant before and after optimization was 3.2 g/L of and 10.9 g/L, respectively. Thus, RSM has increased the yield of biosurfactant to 3.4-fold. The crude biosurfactant decreased the surface tension of water from 72 mN/m to 30.8 mN/m (at 120 mg L(-1)) and achieved a critical micelle concentration (CMC) value of 120 mg L(-1).

Media optimization for biosurfactant production by Rhodococcus erythropolis MTCC 2794: artificial intelligence versus a statistical approach.

This paper entails a comprehensive study on production of a biosurfactant from Rhodococcus erythropolis MTCC 2794. Two optimization techniques—(1) artificial neural network (ANN) coupled with genetic algorithm (GA) and (2) response surface methodology (RSM)—were used for media optimization in order to enhance the biosurfactant yield by Rhodococcus erythropolis MTCC 2794. ANN and RSM models were developed, incorporating the quantity of four medium components (sucrose, yeast extract, meat peptone, and toluene) as independent input variables and biosurfactant yield [calculated in terms of percent emulsification index (% EI24)] as output variable. ANN-GA and RSM were compared for their predictive and generalization ability using a separate data set of 16 experiments, for which the average quadratic errors were ~3 and ~6%, respectively. ANN-GA was found to be more accurate and consistent in predicting optimized conditions and maximum yield than RSM. For the ANN-GA model, the values of correlation coefficient and average quadratic error were ~0.99 and ~3%, respectively. It was also shown that ANN-based models could be used accurately for sensitivity analysis. ANN-GA-optimized media gave about a 3.5-fold enhancement in biosurfactant yield.

Chemoenzymatic synthesis of D(-)phenylglycine using hydantoinase of Pseudomonas desmolyticum resting cells

We screened 125 Pseudomonas strains from our culture collection for the production of hydantoinase activity using DL-phenylhydantoin as a substrate. Pseudomonas desmolyticum NCIM 2112 was found to be the best hydantoinase (dihydropyrimidinase E.C. 3.5.2.2) producer. The enzymatic reactions were carried out using 18-20-h grown cells in nutrient broth and 5-phenylhydantoin as the substrate. Optimization studies for the biotransformation reaction were performed to increase product yield. The optimum pH and temperature for D(-)N-carbamoylphenylglycine production were 9.5 and 30 degrees C, respectively. Biotransformation under these alkaline conditions allowed the complete conversion of 27.0 g l-1 of DL-phenylhydantoin to 26.5 g l-1 of N-carbamoylphenylglycine within 24 h, with a molar yield of 90%. The hydantoinase involved in this biotransformation process was strictly D-stereospecific, because the product isolated was pure D(-)N-carbamoylphenylglycine. This pure product was further chemically converted to D(-)phenylglycine using nitrous acid with an 80% chemical yield. Thus, the overall conversion efficiency of DL-5-phenylhydantoin to D(-)phenylglycine was found to be 65-68%.