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-002105nas a2200265   4500008004100000022001400041245009600055210006900151260006900220300001200289490000700301520118700308653002801495653002001523653001701543653001301560653001701573653001301590100002001603700002201623700002601645700002101671700001701692856013001709       2008                        eng d  a1369-703X00aOptimal xylanase production using Penicilium janthinellum NCIM 1169: a model based approach0 aOptimal xylanase production using Penicilium janthinellum NCIM 1  aPO BOX 564, 1001 LAUSANNE, SWITZERLANDbELSEVIER SCIENCE SAcJUN  a348-3560 v403 a<p>Xylanases are an industrially important class of hydrolytic enzymes that degrade xylans. Production of xylanase from a fungal culture by submerged fermentation and optimization of the operating conditions for maximum activity are the two aims of the present study. Penicillium janthinellum NCIM 1169 with Mandels-Weber medium, sugarcane bagassse (40\#) as a carbon source and beef extract as a nitrogen source were used in the experiments. We did 41 experiments to see the effect of variations in carbon, nitrogen source, pH, and inoculum on xylanase activity. This data was then used to build an input/output model using multiple linear regression, back propagation neural network and lazy learning algorithm. It was found that lazy learning model correlated well in mapping input/output data. This model was then utilized as an objective function in genetic algorithm to find the optimal combination of the operating conditions to get the maximum xylanase activity. It was observed that with carbon source, 1.63%, nitrogen source, 0.16%, pH, 4.1, and inoculum, 5.5%, maximum xylanase activity of 28.98 +/- 1.73 U/ml was achieved. (C) 2008 Elsevier B.V. All rights reserved.</p>10aArtificial intelligence10aEnzyme activity10aFermentation10aModeling10aOptimization10aXylanase1 aMeshram, Mukesh1 aKulkarni, Abhijit1 aJayaraman, Valadi, K.1 aKulkarni, B., D.1 aLele, S., S.  uhttps://library.ncl.res.in/content/optimal-xylanase-production-using-penicilium-janthinellum-ncim-1169-model-based-approach-0