02249nas a2200241   4500008004100000022001400041245010900055210006900164260009900233300001400332490000700346520131600353653001801669653002401687653003301711653002101744100002401765700002801789700002201817700002101839700002101860856012601881       2008                        eng d  a0960-852400aUse of response surface optimization for the production of biosurfactant from rhodococcus spp. MTCC 25740 aUse of response surface optimization for the production of biosu  aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLANDbELSEVIER SCI LTDcNOV  a7875-78800 v993 a<p>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 R-2 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. (C) 2008 Published by Elsevier Ltd.</p>10aBiosurfactant10aMedium optimization10aResponse surface methodology10aRhodococcus spp.1 aMutalik, Snehal, R.1 aVaidya, Bhalchandra, K.1 aJoshi, Renuka, M.1 aDesai, Kiran, M.1 aNene, Sanjay, N.  uhttps://library.ncl.res.in/content/use-response-surface-optimization-production-biosurfactant-rhodococcus-spp-mtcc-2574-002362nas a2200277   4500008004100000022001400041245014500055210006900200260008000269300001200349490000700361520130400368653003001672653001801702653002201720653002301742653003301765653001601798100002101814700002801835700002101863700002201884700002101906700002601927856013101953       2009                        eng d  a1367-543500aMedia optimization for biosurfactant production by rhodococcus erythropolis MTCC 2794: artificial intelligence versus a statistical approach0 aMedia optimization for biosurfactant production by rhodococcus e  aTIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANYbSPRINGER HEIDELBERGcMAY  a747-7560 v363 a<p>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 similar to 3 and similar to 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 similar to 0.99 and similar to 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.</p>10aArtificial neural network10aBiosurfactant10aGenetic algorithm10aMedia optimization10aResponse surface methodology10aRhodococcus1 aPal, Moumita, P.1 aVaidya, Bhalchandra, K.1 aDesai, Kiran, M.1 aJoshi, Renuka, M.1 aNene, Sanjay, N.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/media-optimization-biosurfactant-production-rhodococcus-erythropolis-mtcc-2794-artificial-0