Application of Response Surface Methodology for Xanthan Gum and Biomass Production Using Xan-thomonas campestris

Authors

1 Department of Food Science and Technology, College of Agriculture, Shiraz University, Shir

2 Department of Food Science and Technology, College of Agriculture, Shiraz University, Shiraz, Iran Seafood Processing Research group, College of Agriculture, Shiraz University, Shiraz, Iran

3 Department of Food Science and Technology, College of Agriculture, Shiraz University, Shiraz, Iran

Abstract

   Xanthan gum is an extracellular polysaccharide produced by various Xanthomonas species such as X. campestris. The objective of present study was to investigate the influence of different carbon and nitrogen sources on xanthan gum production by X. campestris. Using an experimental Response Surface Methodology (RSM) complemented with a Central Composite Design (CCD), the impact of peptone, lactose, glucose and ammonium nitrate in medium were estimated for their individual and interactive effects on biomass and xanthan gum production. The optimal concentrations of peptone, lactose, glucose and ammonium nitrate for xanthan gum yield and biomass production was determined as 9.25 g/l, 53.37 mmol, 29.31 mmol and 4.58 g/l for xanthan gum yield and 6.77 g/l, 52.65 mmol, 38.12 mmol and 3.54 g/l for biomass production. Under the optimum experimental conditions, the xanthan gum yield reached to its maximum value (8.42 g/l). The results provide the support data for xanthan gum production on a large scale. 

Keywords

References

Garcıa-Ochoa F., Santos V., Casas J., Gomez E., 2000. Xanthan gum: production, recovery and properties. Biotechnol adv. 18, 549-579.
Freitas F., Alves V.D., Reis M.A.M., 2011. Advances in bacterial exopolysaccharides: from production to biotechnological applications. Trends Biotechnol. 29, 388-398.
Psomas S.K., Liakopoulou-Kyriakides M., Kyriakidis D.A., 2007. Optimization study of xanthan gum production using response surface methodology. Biochem Eng J. 35, 273-280.
Whitman W.B., Goodfellow M., Kämpfer P., Busse H.J., Trujillo M.E., Ludwig W., Suzuki K.i., Parte A., 2012. Bergeyââ‚‌™s manual of systematic bacteriology (Vol. 5): The Actinobacteria (Bergeyââ‚‌™s Manual/Systemic Bacterio- logy,ââ‚‌ 2nd Edition, Springer, New York.
Eriksson L., Johansson E., Kettaneh-wold N., 2008. Design of experiments: principles and applications. Umea: Umetrics academy. 8ââ‚‌“24.
Czitrom V., 1999. One-factor-at-a-time versus designed experiments. Am Stat. 53, 126ââ‚‌“131.
Anderson-cook C.M., Borror C.M., Montgomery D.C., 2009. Response surface design evaluation and comparison. J Stat Plan Infere. 139, 629ââ‚‌“641.
Box G.E.P., Draper N.R., 1987. Empirical model building and response surfaces. New York: John Wiley and Sons; 477.
Gan C.Y., Latiff A.A., 2011. Extraction of antioxidant pectic-polysaccharide from mangosteen (Garciniamangostana) rind: Optimization using response surface methodology. Carbohyd Polym. 83, 600-607.
Sun Y., Liu J., Kennedy J.F. 2010. Application of response surface methodology for optimization of polysaccharides production parameters from the roots of Codonopsispilosula by a central composite design. Carbohyd Polym. 80, 949-953.
Ye C.L., Jiang C.J. 2011. Optimization of extraction process of crude polysaccharides from Planta goasiatica L. by response surface methodology. Carbohyd Polym. 84, 495-502.
Dong Z., Gu L., Zhang J., Wang M., Du G., Chen J., Li H., 2014. Optimisation for high cell density cultivation of Lactobacillus salivarius BBE 09-18 with response surface methodology. Int Dairy J. 34, 230-236.
Hirkude J.B., Padalkar A.S., 2014. Performance optimization of CI engine fuelled with waste fried oil methyl ester-diesel blend using response surface methodology. Fuel. 119, 266-273.
Gharibzahedi S.M.T., Mousavi S.M., Hamedi M., Khodaiyan F., 2013. Application of response surface modeling to optimize critical structural components of walnut-beverage emulsion with respect to analysis of the physicochemical aspects. Food Bioprocess Tech. 6, 456-469.
Ye L., Yang M., Xu L., Guo C., Li L., Wang D., 2014. Optimization of inductive angle sensor using response surface methodology and finite element method. Measurement. 48, 252-262.
Zhang Y.B., Wang L.H., Zhang D.Y., Zhou L.L., Guo Y.X., 2014. Ultrasound-assisted extraction and purification of schisandrin B from Schisandra chinensis (Turcz.) Baill seeds: Optimization by response surface methodology. Ultrason Sonochem. 21, 461-466.
Qiu P., Cui M., Kang K., Park B., Son Y., Khim E., Jang M., Khim J., 2014. Application of Box-Behnken design with response surface methodology for modeling and optimizing ultrasonic oxidation of arsenite with H2O2. Cent Eur J Chem. 12, 164-172.
Gharibzahedi S.M.T., Razavi S.H., Mousavi S.M., 2013. Psyllium husk gum: An attractive carbohydrate biopolymer for the production of stable canthaxanthin emulsions. Carbohyd Polym. 92, 2002-2011.
Lu J., Feng X., Han Y., Xue C., 2014. Optimization of subcritical fluid extraction of carotenoids and chlorophyll a from Laminaria japonica Aresch by response surface methodology. J Sci Food Agr. 94, 139-145.
Honary S., Ebrahimi P., Hadianamrei R., 2014. Optimization of particle size and encapsulation efficiency of vancomycin nanoparticles by response surface methodology. Pharm Dev Tech. 19, 987-998.
Deswal A., Deora N.S., Mishra H.N., 2014. Optimization of enzymatic production process of oat milk using response surface methodology. Food Bioprocess Tech. 7, 610-618.
Farhadi G.B.N., Khosravi-Darani K., Nejad B.N., 2012. Enhancement of Xanthan production on date extract using response surface methodology. Asian J Chem. 24, 3887-3890.
Ben Salah R., Chaari K., Besbes S., Ktari N., Blecker C., Deroanne C., Attia H., 2010. Optimisation of xanthan gum production by palm date (Phoenix dactylifera L.) juice by-products using response surface methodology. Food Chem. 121, 627-633.
Rosalam S., England R. 2006. Review of xanthan gum production from unmodified starches by Xanthomonas camprestris sp. Enzyme Microb Tech. 39, 197ââ‚‌“207.

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History

  • Receive Date: 04 July 2015
  • Revise Date: 08 March 2021
  • Accept Date: 29 October 2018

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