Comparative analyses on medium optimization using one-factor-at-a-time, response surface methodology, and artificial neural network for lysine–methionine biosynthesis by Pediococcus pentosaceus RF-1

References

• Shakoori FR, Butt AM, Ali NM, et al. Optimization of fermentation media for enhanced amino acids production by bacteria isolated from natural sources. Pak J Zool. 2012;44:1145–1157.
   Web of Science ®Google Scholar
• Khan SH, Rasool G, Nadeem S. Bioconversion of cane molasses into amino acids. Pak J Agric Sci. 2006;43:157–161.
   Google Scholar
• Khattak FM, Pasha TN, Hayat Z, et al. Enzymes in poultry nutrition. J Anim Poult Sci. 2006;16:1–7.
   Google Scholar
• Liu SQ, Holland R, Crow VL. The potential of dairy lactic acid bacteria to metabolise amino acids via non-transaminating reactions and endogenous transamination. Int J Food Microbiol. 2003;86:257–269.
   PubMed Web of Science ®Google Scholar
• Fumiere O, Veys P, Boix A, et al. Methods of detection, species identification and quantification of processed animal proteins in feedingstuffs. Biotechnol Agric Soc Environ. 2009;13:59–70.
   Web of Science ®Google Scholar
• Kranenburg RV, Klereebezem MB, Vlieg JVL, et al. Flavour formation from amino acids by lactic acids bacteria: predictions from genome sequence analysis. Int Dairy J. 2002;12:111–121.
   Web of Science ®Google Scholar
• Leuchtenberger W, Huthmacher K, Karlheinz D. Biotechnological production of amino acids and derivatives: current status and prospects. Appl Microbiol Biotechnol. 2005;69:1–8.
   PubMed Web of Science ®Google Scholar
• Basri M, Rahman RNZRA, Ebrahimpour A, et al. Comparison of estimation capabilities of response surface methodology (RSM) with artificial neural network (ANN) in lipase-catalyzed synthesis of palm-based wax ester. BMC Biotechnol. 2007;7:53.
   PubMed Web of Science ®Google Scholar
• Bibhu PP, Ali M, Salem J. Fermentation process optimization. Res J Microbiol. 2007;2:201–208.
   Google Scholar
• Parra R, David A, Naresh M. Medium optimization for the production of the secondary metabolite squalestatin S1 by a Phoma species combining orthogonal design and response surface methodology. Enzyme Microbiol Technol. 2005;37:704–711.
   Web of Science ®Google Scholar
• Schmidt FR. Optimization and scale up of industrial fermentation processes. Appl Microbiol Biotechnol. 2005;68:425–435.
   PubMed Web of Science ®Google Scholar
• Nor MN, Mohamad R, Foo HL, et al. Improvement of folate biosynthesis by lactic acid bacteria using response surface methodology. Food Technol Biotechnol. 2010;48:243–250.
   Web of Science ®Google Scholar
• Hu Y, Qin H, Zhan Z, et al. Optimization of Saccharomyces boulardii production in solid state fermentation with response surface methodology. Biotechnol Biotechnol Equip. 2016;30(1):173–179.
   Web of Science ®Google Scholar
• Rosfarizan M, Ariff AB, Hassan MA, et al. Kojic acid production by Aspergillus flavus using gelatinized and hydrolyzed sago starch as carbon sources. Folia Microbiol. 1998;43:459–464.
   PubMed Web of Science ®Google Scholar
• Dasari VRRK, Donthireddy SRR, Nikku MY, et al. Optimization of medium constituents for Cephalosporin C production using response surface methodology and artificial neural networks. J Biochem Technol. 2009;1: 69–74.
   Google Scholar
• Mohamed MS, Tan JS, Mohamad R, et al. Comparative analyses of response surface methodology and artificial neural network on medium optimization for Tetr aselmis sp. FTC209 grown under mixotrophic condition. Sci World J. 2013;1–14.
   Web of Science ®Google Scholar
• Tan JS, Ramanan RN, Ling TC, et al. Comparative of predictive capabilities of response surface methodology and artificial neural network for optimization of periplasmic interferon-α2b production by recombinant Escherichia coli. Minerva Biotechnol. 2010;22:63–73.
   Web of Science ®Google Scholar
• Basheer IA, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods. 2000;43:3–31.
   PubMed Web of Science ®Google Scholar
• Dziuba B, Nalepa B. Identification of lactic acid bacteria and propionic acid bacteria using ftir spectroscopy and artificial neural networks. Food Technol Biotechnol. 2012;50:399–405.
   Web of Science ®Google Scholar
• Poirazi P, Leroy F, Georgalaki MD, et al. Use of artificial neural networks and a gamma-concept-based approach to model growth of and bacteriocin production by Streptococcus macedonicus aca-dc 198 under simulated conditions of kasseri cheese production. Appl Environ Microbiol. 2007;73:768–776.
   PubMed Web of Science ®Google Scholar
• Moghaddam MG, Khajeh M. Comparison of Response Surface Methodology and Artificial Neural Network in Predicting the Microwave-Assisted Extraction Procedure to Determine Zinc in Fish Muscles. Food Nutr Sci. 2011;2:803–808.
   Google Scholar
• Moghaddam MG, Ahmad FBH, Basri M, et al. Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester. Electron J Biotechnol. 2010;12:1–12.
   Google Scholar
• Yadav M, Sehrawat N, Sangwan A, et al. Artificial Neural Network (ANN): application in media optimization for industrial microbiology and comparison with Response Surface Methodology (RSM). Adv Appl Sci Res. 2013;4:457–460.
   Google Scholar
• Nelofer R, Ramanan RN, Abdul Rahman RNZR, et al. Comparison of the estimation capabilities of response surface methodology and artificial neural network for the optimization of recombinant lipase production by E. coli BL21. J Ind Microbiol Biotechnol. 2012;39:243–254.
   PubMed Web of Science ®Google Scholar
• Bordoloi H, Sarma KK. Protein structure prediction using artificial neural network. Special Issue Int J Comput Appl. 2011;3:22–24.
   Google Scholar
• Uppada SR, Balu A, Gupta AK, et al. Modeling lipase production from co-cultures of lactic acid bacteria using neural networks and support vector machine with genetic algorithm optimization. Int J Emerg Technol Comput Appl Sci. 2014;9:38–43.
   Google Scholar
• Norfarina MN, Chwen LT, Raha AR, et al. 2014. Evaluation of lactic acid bacteria as amino acid producer for poultry feed additive formulation. Poster presented in the 35th Annual Conference of Malaysian Society of Animal Production (MSAP), Riverside Majestic Hotel Kuchingth. Sarawak, Malaysia.
   Google Scholar
• Abdul Razack S, Velayutham V, Thangavelu V. Medium optimization for the production of exopolysaccharide by Bacillus subtilis using synthetic sources and agro waste. Turkish J Biol. 2013;37:280–288.
   Web of Science ®Google Scholar
• Miller GL. Use of Dinitrosalycilic acid reagent for determination of reducing sugar. Anal Chem. 1959;31:426.
   Web of Science ®Google Scholar
• Chinard FP. Paper chromatography. J Biol Chem. 1952;199:91.
   PubMed Web of Science ®Google Scholar
• Mao Y, Tian C, Zhu J. Production of novel biopolymer by culture of B. cereus B11 using molasses wastewater and its use for dye removal. Adv Mater Res. 2011;230–232:1119–1122.
   Google Scholar
• Rebah FB, Miled N. Fish processing wastes for microbial enzyme production: a review. Biotechnology. 2013;3:255–265.
   Google Scholar
• Meijer GAL, Meulen JVD, Vuuren AMV. Glutamine is a potentially limiting amino acid for milk production in dairy cows: a hypothesis. Metabolism. 1993;3:358–364.
   Web of Science ®Google Scholar
• Pollack MA, Lindner M. Glutamine and glutamic acid as growth factors for lactic acid bacteria. J Biol Chem. 1942;143:655–661.
   Google Scholar
• Dicks LMT, Endo A. Taxonomic status of lactic acid bacteria in wine and key characteristics to differentiate species. South Afr J Enol Viticol. 2009;30:72–90.
   Web of Science ®Google Scholar
• Fernandez PAA, Saguir FM, De Nadra MCM. Effect of amino acids and peptides on growth of Pediococcus pentosaceus from wine. Latin Am Appl Res. 2003;33:225–229.
   Web of Science ®Google Scholar
• Shihab K. A backpropagation neural network for computer network security. J Comput Sci. 2006;2:710–715.
   Google Scholar
• Hayek SA, Ibrahim SA. Current limitations and challenges with lactic acid bacteria: a review. Food Nutr Sci. 2013;4:73–87.
   Google Scholar