Enhanced Production of Xylitol from Corncob byPachysolen tannophilusUsing Response Surface Methodology

Abstract

Optimization of the culture medium and process variables for xylitol production using corncob hemicellulose hydrolysate by Pachysolen tannophilus (MTTC 1077) was performed with statistical methodology based on experimental designs. The screening of nine nutrients for their influence on xylitol production was achieved using a Plackett-Burman design. Peptone, xylose, MgSO4·7H2O, and yeast extract were selected based on their positive influence on xylitol production. The selected components were optimized with Box-Behnken design using response surface methodology (RSM). The optimum levels (g/L) were peptone: 6.03, xylose: 10.62, MgSO4·7H2O: 1.39, yeast extract: 4.66. The influence of various process variables on the xylitol production was evaluated. The optimal levels of these variables were quantified by the central composite design using RSM, for establishment of a significant mathematical model with a coefficient determination of R 2 = 0.91. The validation experimental was consistent with the prediction model. The optimum levels of process variables were temperature (36.56°C), pH (7.27), substrate concentration (3.55 g/L), inoculum size (3.69 mL), and agitation speed (194.44 rpm). These conditions were validated experimentally which revealed an enhanced xylitol yield of 0.80 g/g.

Optimizing culture conditions for production of intra and extracellular inulinase and invertase from Aspergillus niger ATCC 20611 by response surface methodology (RSM)

“…This variation reflected the importance of optimization to attain higher productivity. From the previous studies optimizing the medium by one factor at a time method, the variables, namely, initial pH, temperature, inoculum size and agitation speed, were selected for further optimization 15, 17. In this research, we tried to analyze, model, and interpret the experimental data using RSM as a mathematical modeling system.…”

“…Combined interactions of medium physical parameters for the production of the desired product are large and the optimum process conditions may be developed using an effective experimental design procedure. 12 , 15 RSM, which is a collection of statistical techniques for design of experiments, building models, measured responses, evaluating the effects and relationship between clusters of controlled experimental factors and searching for the optimum conditions, has successfully been used in the optimization of bioprocesses. 15 , 16 A prior knowledge with understanding of the related bioprocesses is necessary for achieving a more realistic model.…”

“… 12 , 15 RSM, which is a collection of statistical techniques for design of experiments, building models, measured responses, evaluating the effects and relationship between clusters of controlled experimental factors and searching for the optimum conditions, has successfully been used in the optimization of bioprocesses. 15 , 16 A prior knowledge with understanding of the related bioprocesses is necessary for achieving a more realistic model. In the present work, four independent variables were studied at five different levels ( Table 1 ) and screened in 21 experimental runs ( Table 2 ), then insignificant ones were eliminated in order to obtain a smaller and more fitting set of factors.…”

Bioconversion of Non-Detoxified Hemicellulose Hydrolysates to Xylitol by Halotolerant Yeast Debaryomyces nepalensis NCYC 3413

“…by C. guilliermondii at optimized conditions with 0.9 g g -1 yield and 0.5 g L -1 h -1 productivity [35]. Though the present results are not comparable ( Figure 3D), but intriguing, the possible high production after detoxification and subsequent optimization of fermentation conditions.…”

Optimization of <i>Synechococcus</i> sp. VDW Cultivation with Artificially Prepared Shrimp Wastewater for Ammonium Removal and Its Potential for Use As a Biofuel Feedstock

“…The quality of fit of the polyno-mial model equation was expressed by the coefficient of determination R 2 and the adjusted R 2 R 2 adj , and its statistical significance was assessed with a P-value. The optimum values of the selected variables were obtained by analyzing the contour plots and also by analyzing the quadratic polynomial equation 29 .…”