Optimization of fermentation media for xanthan gum production from Xanthomonas campestris using Response Surface Methodology and Artificial Neural Network techniques

Velu, Selvi

Abstract

Xanthan gum produced by the bacterium Xanthomonas campestris ATCC 29497(NCIM 5028) using synthetic substrate has been studied. The nutritional requirements for xanthan gum production have been optimized using Response Surface Methodology (RSM) and Artificial Neural Network. The medium components considered are glucose, yeast extract, peptone, malt extract, KH2PO4, MgSO4.7H2O, Citric acid, (NH4)2SO4, H3BO3, ZnCl2, CaCO3, Na2SO4, FeCl3.6H2O, NH4NO3 and MgCl2. Initial screening using Plackett-Burman statistical design identified the following five components glucose, peptone, KH2PO4, (NH4)2SO4, and FeCl3.6H2O as significantly influencing the xanthan gum production. RSM-Central Composite Design has been applied to determine the mutual interactions between these five media components and its optimal levels for xanthan gum production. The optimal concentrations for enhanced production of xanthan gum are found to be: glucose, 40.72g/L; peptone, 9.84g/L; KH2PO4, 4.976 g/L; (NH4)2SO4, 3.024 g/L and FeCl3.6H2O, 0.1134 g/L. Artificial neural network (ANN) is employed for the modelling of xanthan gum production by Xanthomonas campestris. A feed forward back propagation neural network tool is used to optimize media components for xanthan gum production. ANN predicted and RSM predicted values are compared with the experimental values.

Keyword(s)

Artificial neural network; Batch fermentation; FTIR; Response surface methodology; Xanthomonas campestris; Xanthan gum

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