Astaxanthin biosynthesis: A two-step optimization approach and model construction with Response Surface Methodology and Artificial Neural Network
Abstract
The
first part of this research is investigating and comparing yield of a synthetic
medium submerged three sugars (glucose, fructose and sucrose) at four different
concentrations and solid fermentation systems with wheat bran and lentil waste
for biosynthesis of astaxanthin (ASX) pigment by Xanthophyllomyces dendrorhous ATCC 24202 and Sporidiobolus salmonicolor ATCC 24259 microorganisms. The second
part is modeling and optimizing the most efficient biosynthesis depending on
waste, yeast and production variables consisted of moisture content, pH and
temperature using a design matrix. The yields produced by X. dendrorhous were 51.88 µg of ASX/g glucose for the submerged
medium with the least glucose, and 210.49 µg of ASX/g glucose for the wheat bran
fermentation system. It was understood that the yield values of the
submerged systems were
lower and there was no requirement for the addition of any
supplement to the waste systems. It was found that R2=0.9869 was
the highest value with the maximum predicted ASX amount of 109.23 µg of ASX/g
wheat bran with X. dendrorhous
using Artificial Neural Network modeling and the moisture content was the most
significant production parameter.
Keywords
Astaxanthin biosynthesis,Neural network,Optimization,Response Surface Methodology,Submerged system
References
- Aber, A.B., Damtew, W., Emire, S.A. (2012). Evaluation of growth kinetics and biomass yield efficiency of industrial yeast strains. Archives of Applied Science Research, 4 (5), 1938−1948. http://scholarsresearchlibrary.com/archive.html.
- Amorim-Carrilho, K.T., Cepeda, A., Fente, C., Regal, P. (2014). Review of methods for analysis of carotenoids. TrAC Trends in Analytical Chemistry,56, 49−73. https://doi.org/10.1016/j.trac.2013.12.011.
- An, J., Gao, F., Ma, Q., Xiang, Y., Ren, D., Lu, J. (2017). Screening for enhanced astaxanthin accumulation among Spirulina platensis mutants generated by atmospheric and room temperature plasmas. Algal Research, 25, 464–472. https://doi.org/10.1016/j.algal.2017.06.006.
- Ananda, N., Vadlani, P.V. (2011). Carotenoid value addition of cereal products by monoculture and mixed-culture fermentation of Phaffia rhodozyma and Sporobolomyces roseus. Cereal Chemistry, 88, 467–472. https://doi.org/10.1094/CCHEM-04-11-0053.
- Arroyo-López, F.N., Orlić, S., Querol, A., Barrio, E. (2009). Effects of temperature, pH and sugar concentration on the growth parameters of Saccharomyces cerevisiae, S. kudriavzevii and their interspecific hybrid. International Journal of Food Microbiology, 131 (2-3), 120–127.https://doi.org/10.1016/j.ijfoodmicro.2009.01.035.
- Babitha, S., Soccol, C.R., Pandey, A. (2007). Solid-state fermentation for the production of Monascus pigments from jackfruit seed. Bioresource Technology, 98 (8), 1554−1560.https://doi.org/10.1016/j.biortech.2006.06.005.
- Bailey, J.E., Ollis, D.F. (1986). Biochemical Engineering Fundamentals. 2nd ed. McGraw-Hill, Singapore, 984 pages.
- Basri, M., Rahman, R.N.Z.R.A., Ebrahimpour, A., Salleh, A.B., Gunawan, E.R., Rahman, M.B.A. (2007). Comparison of estimation capabilities of response surface methodology (RSM) with artificial neural network (ANN) in lipase-catalyzed synthesis of palm-based wax ester. BMC Biotechnology, 7 (53), 1–14. http://www.biomedcentral.com/1472-6750/7/53.
- Baş, D., Boyacı, I.H. (2007). Modeling and optimization I: Usability of response surface methodology. Journal of Food Engineering, 78 (3), 836−845. https://doi.org/10.1016/j.jfoodeng.2005.11.024.
- Carlson, M. (1987). Regulation of sugar utilization in Saccharomyces species. Journal of Bacteriology, 169 (11), 4873−4877. doi:10.1128/jb.169.11.4873-4877.1987, PMCID: PMC213879.
