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ABSTRACT: 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).
Bioresource Technology 12/2008; 99(16):7875-80. · 4.98 Impact Factor
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ABSTRACT: Laccase production by solid-state fermentation (SSF) using an indigenously isolated white rot basidiomycete Ganoderma sp. was studied. Among the various agricultural wastes tested, wheat bran was found to be the best substrate for laccase production. Solid-state fermentation parameters such as optimum substrate, initial moisture content, and inoculum size were optimized using the one-factor-at-a-time method. A maximum laccase yield of 2,400 U/g dry substrate (U/gds) was obtained using wheat bran as substrate with 70% initial moisture content at 25 degrees C and the seven agar plugs as the inoculum. Further enhancement in laccase production was achieved by supplementing the solid-state medium with additional carbon and nitrogen source such as starch and yeast extract. This medium was optimized by response surface methodology, and a fourfold increase in laccase activity (10,050 U/g dry substrate) was achieved. Thus, the indigenous isolate seems to be a potential laccase producer using SSF. The process also promises economic utilization and value addition of agro-residues.
Applied Biochemistry and Biotechnology 11/2007; 143(1):16-26. · 1.94 Impact Factor
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ABSTRACT: Response surface methodology (RSM) is the most preferred method for fermentation media optimization so far. In last two decades, artificial neural network-genetic algorithm (ANN-GA) has come up as one of the most efficient method for empirical modeling and optimization, especially for non-linear systems. This paper presents the comparative studies between ANN-GA and RSM in fermentation media optimization. Fermentative production of biopolymer scleroglucan has been chosen as case study. The yield of scleroglucan was modeled and optimized as a function of four independent variables (media components) using ANN-GA and RSM. The optimized media produced 16.22 ± 0.44 g/l scleroglucan as compared to 7.8 ± 0.54 g/l with unoptimized medium.Two methodologies were compared for their modeling, sensitivity analysis and optimization abilities. The predictive and generalization ability of both ANN and RSM were compared using separate dataset of 17 experiments from earlier published work. The average % error for ANN and RSM models were 6.5 and 20 and the CC was 0.89 and 0.99, respectively, indicating the superiority of ANN in capturing the non-linear behavior of the system. The sensitivity analysis performed by both methods has given comparative results. The prediction error in optimum yield by hybrid ANN-GA and RSM were 2% and 8%, respectively.
Biochemical Engineering Journal.
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ABSTRACT: An artificial neural network (ANN) approach was used to model dry yeast cell mass (Saccharomyces cerevisiae NCIM 3458) and its glucan contents. The dry cell mass and yield of glucan depend mainly on concentration of media components. The mathematical relationship between them was not well established. This relationship between the concentration of media components with yield of glucan and with dry cell mass is important from a process optimization and control point of view. In the present work an ANN model was developed, which incorporated the effect of the following media components: glucose, peptone, yeast extract, malt extract, Mn2+ and Mg2+ as input parameters and the yield glucan and dry biomass is obtained after optimizing model parameters. The predictive capacity of the trained network was tested using separate data set (testing set), which is not used for the training. The average quadratic error (AQE) for trained network was 0.0012 and 0.003 for glucan and biomass, respectively. The network predicts the yield of glucan within the range ±3.5% and biomass within the rage ±5.5% of the experimental values. The trained network showed comparable trends in change of yield of glucan with change in respective input parameters.
Process Biochemistry.
