02794nas a2200277   4500008004100000022001400041245014800055210006900203260009900272300001400371490000700385520173200392653003002124653002202154653001702176653002202193653002802215653002302243653002002266100002102286700002002307700002202327700001802349700001702367856013202384       2006                        eng d  a1359-511300aOptimization of fermentation media for exopolysaccharide production from lactobacillus plantarum using artificial intelligence-based techniques0 aOptimization of fermentation media for exopolysaccharide product  aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLANDbELSEVIER SCI LTDcAUG  a1842-18480 v413 a<p>A Lactobacillus strain was isolated from the fermented Eleusine coracana. This strain was characterized as Lactobacillus plantarum and was found to produce an exopolysaccharide (EPS) in quantitative amounts. The objective of the present paper is to determine optimum media composition and inoculum volume for the stated fermentative production of the EPS. A hybrid methodology comprising the Plackett-Burman (PB) design method, artificial neural networks (ANN) and genetic algorithms (GA) was utilized. Specifically, the PB, ANN and GA forrnalisms were used for identifying influential media components, modeling non-linear process and optimizing the process, respectively. More specifically, the PB method was used to determine those media components, which significantly influence the EPS yield. By ignoring the less influential media components, the dimensionality of the input space of the process model could be reduced significantly. Out of the five media components only three were found influential namely, lactose, casein hydrolysate and triammonium citrate. Next, an ANN-based process model was developed for approximating the non-linear relationship between the fermentation operating variables and the EPS yield. The average % error and correlation coefficient for the developed ANN model were 4.8 and 0.999, respectively. The input parameters of ANN model were subsequently optimized using the GA formalism for obtaining maximum EPS yield in batch fermentation. The optimized media composition has predicted the yield of 7.01 g/l. The GA-optimized solution comprising media composition and inoculum volume was verified experimentally and it comes out be 7.14 g/l. (c) 2006 Elsevier Ltd. All rights reserved.</p>10aArtificial neural network10aexopolysaccharide10aFermentation10aGenetic algorithm10aLactobacillus plantarum10aMedia optimization10aPlackett-Burman1 aDesai, Kiran, M.1 aAkolkar, S., K.1 aBadhe, Yogesh, P.1 aTambe, S., S.1 aLele, S., S.  uhttps://library.ncl.res.in/content/optimization-fermentation-media-exopolysaccharide-production-lactobacillus-plantarum-using-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