Modeling and optimization of β-cyclodextrin production by Bacillus licheniformis using artificial neural network and genetic algorithm

Iranian Journal of Biotechnology

Volumn 11, Issue 4, 2013, Pages 223-232

  Sanjari, Samaneh ^a   Naderifar, Abbas ^a   Pazuki, Gholamreza ^a  

^a AMIRKABIR UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

cyclodextrin production; Artificial neural network; Bacillus licheniformis; Genetic algorithm; Modeling; Optimization

Indexed keywords

BETA CYCLODEXTRIN; PHENOLPHTHALEIN;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BACILLUS LICHENIFORMIS; COMPUTER PROGRAM; CONCENTRATION (PARAMETERS); GENETIC ALGORITHM; LEARNING ALGORITHM; NONHUMAN; PH; SENSITIVITY ANALYSIS; SPECTROPHOTOMETRY; TEMPERATURE;

EID: 84888080278     PISSN: 17283043     EISSN: None     Source Type: Journal    
DOI: 10.5812/ijb.11272     Document Type: Article

References (15)

1. Safarikova M, Atanasova N, Ivanova V, Weyda F, Tonkova A. Cyclodextrin glucanotransferase synthesis by semicontinuous cultivation of magnetic biocatalysts from cells of Bacillus circulans ATCC 21783. Proc Biochem. 2007;42(10):1454-1459.
2. Pazzetto R, de Oliveira Delani TC, Fenelon VC, Matioli G. Cyclodextrin production by Bacillus firmus strain 37 cells immobilized on loofa sponge. Proc Biochem. 2011;46(1):46-51.
3. Letsidid R, Sun T, Mu W, et al. Production of a thermoactive β-cyclodextrin glycosyltransferase with a high starch hydrolytic activity from an alkalitolerant Bacillus Licheniformis Sk 13.002 Strain. Asian J Biotechnol. 2011;3(3):214-25.
4. Bonilha PRM, Menocci V, Goulart AJ, Polizeli MdLTdM, Monti Rs. Cyclodextrin glycosyltransferase from Bacillus licheniformis: optimization of production and its properties. Brazilian J Microbiol. 2006;37:317-23.
5. Sivakumar N, Banu S. Standardization of optimum conditions for cyclodextrin glycosyltransferase production. International Conference on Food Engineering and Biotechnology.; Singapore: ACSIT Press: 2011. p. 102-6.
6. Mitchell M. London, England.: A Bradford Book The MIT Press; 1999. An introduction to genetic algorithm.
7. Lappin LL. Duxbury Press; 1997. Modern engineering statistics.
8. Basheer IA, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Met. 2000;43(1):3-31.
9. Zhang Y, Xu J, Yuan Z, Xu H, Yu Q. Artificial neural network-genetic algorithm based optimization for the immobilization of cellulase on the smart polymer Eudragit L-100. Bioresour Technol. 2010;101(9):3153-58.
10. Baishan F, Hongwen C, Xiaolan X, Ning W, Zongding H. Using genetic algorithms coupling neural networks in a study of xylitol production: medium optimisation. Proc Biochem. 2003;38(7):979-85.
11. Mazzer C, Ferreira LR, Rodella JR, Moriwaki C, Matioli G. Cyclodextrin production by Bacillus firmus strain 37 immobilized on inorganic matrices and alginate gel. Biochem Eng J. 2008;41(1):79-86.
12. Mahmoud O, Anwar F, Salami MJ. Learning algorithm effect on multilayer feed forward artificial neural network performance in image coding. J Eng Sci Technol. 2007;2(2):188-199.
13. Chaibakhsh N, Rahman MBA, Vahabzadeh F, Abd-Aziz S, Basri M, Salleh A. Optimization of operational conditions for adipate ester synthesis in a stirred tank reactor. Biotechnol Bioproc Engin. 2010;15(5):846-53.
14. Shojaeefard MH, Akbari M, Tahani M, Farhani F. Sensitivity Analysis of the Artificial Neural Network Outputs in Friction Stir Lap Joining of Aluminum to Brass. Ad Material Sci Eng. 2013:1-8.
15. Fujikawa H, Kai A, Morozumi S. A new logistic model for Escherichia coli growth at constant and dynamic temperatures. Food Microbiol. 2004;21(5):501-9.