An application of fuzzy linear modeling: prediction of uncertainty for beta-glucan content
Beta-glucan (BG) has positive health effects for the mamalians. However, the BG sources have limited content of it. Besides, the production of the BG has stringent procedures with low productivity. Economical production of the BG needs the improvement of the BG production steps. In this study, it is aimed to improve the BG content during the first step of the BG production, microorganism growth step, by obtaining the optimal values of additive materials (EDTA, CaCl2 and Sorbitol). For this purpose, the experimental data sets with replicated response measures (RRM) are obtained at spesific levels of EDTA, CaCl2 and Sorbitol. Fuzzy modeling, a flexible modeling approach, is applied on the experimental data set because of the small sized data set and diffulty of satisfying probabilistic modeling assumptions. The predicted fuzzy function is obtained according to the fuzzy least squares approach. In order to get the optimal values of EDTA, CaCl2 and Sorbitol, the predicted fuzzy function is maximized based on multi-objective optimization (MOO) approach. By using the optimal values of EDTA, CaCl2 and Sorbitol, the uncertainty for predicted BG content is evaluated from the economic perspective.
[1] Mantovani, M.S., Bellini, M.F., Angeli, J.P.F., Oliveira, R.J., Silva, A.F. & Ribeiro, L.R. (2008). β-Glucans in promoting health: Prevention against mutation and cancer. Mutation Research 658, 154– 161.
[2] Chen, J. & Raymond, K. (2008). Beta-glucans in the treatment of diabetes and associated cardiovascular risks.Vasc. Health Risk Manag. 4(6): 1265–1272.
[3] Magnani, M.,Gomez, R.J.H.C, MoriM.P.,Kuasne, H., Gregorio, E.P., Libos, F.Jr.& Colus, I.M.S.(2011). Protective effect of carboxymethyl-glucan (CM-G) against DNA damage in patients with advanced prostate cancer. Genet. Mol. Biol. 34(1), 131–135.
[4] Satrapai, S. & Suphantharika, M. (2007). Influence of spent brewer’s yeast β-glucan on gelatinization and retrogradation of rice starch. Carbohyd. Polym. 67:500-510.
[5] Hahn-Hägerdal, B., Karhumaa, K., Larsson, C.U., Gorwa-Grauslund, M., Görgens, J. & van Zyl, W.H.(2005). Role of cultivation media in the development of yeast strains for large scale industrial use.Microbial Cell Factories, 4:31, 1-16.
[6] Naruemon, M., Romanee, S., Cheunjit, P., Xiao, H., Mclandsborough, L.A. & Pawadee, M., (2013). Influence of additives on Saccharomyces cerevisiae β-Glucan production, International Food Research Journal, 20, 1953-1959.
[7] Sabuncu, N. (2016). β-glukan içeriğinin arttırılması için S.cerevisiae üretilen bir biyoreaktörde çoğalma koşullarının incelenmesi ve pH kontrolü . Master Thesis. Ankara University.
[8] Aguilar-Uscanga, B. & François, J.M. (2003). A study of theyeast cell wall composition and structure in response to growth conditions and mode of cultivation, Letters in Applied Microbiology, 37, 268–274.
[9] Klis, F.M., Boorsma, A. & De Groot, P.W.J. (2006). Cell wall construction in Saccharomyces cerevisiae. Yeast, 23, 185–202.
[10] Javmen, A., Grigiskis, S. & Gliebute, R. (2012). β- glucan extraction from Saccharomyces cerevisiae yeast using Actinomyces rutgersensis 88 yeast lyzing enzymatic complex. Biologija. 58(2), 51–59.
[11] Lee, J.N., Lee, D.Y., Ji, I.H., Kim, G.E., Kim, H.N., Shon, J., Kim, S. & Kim, C.W. (2001) Purification of Soluble β-Glucan with Immune-enhancing Activity from the Cell Wall of Yeast. Bioscience, Biotechnology, and Biochemistry. 65(4), 837-841.
[12] Hunter, K. W. Jr., Gault, R. A. & Berner, M. D.(2002). Preparation of microparticulate β‐glucan from Saccharomyces cerevisiae for use in immune potentiation. Letters in Applied Microbiology, 35(4), 267-271.
[13] Pelizon, A. C., Kaneno, R., Soares, A. M. V. C., Meira, D. A. & Sartori, A. (2005). Immunomodulatory activities associated with β - glucan derived from Saccharomyces cerevisiae. Physiological Research, 54(5), 557-564.
[14] Shokri, H., Asadi, F. & Khosravi, A. R. (2008). Isolation of β-glucan from the cell wall of Saccharomyces cerevisiae. Natural Product Research, 22(5), 414-421.
[15] Wenger, M. D., DePhillips, P. & Bracewell, D. G.(2008). A microscale yeast cell disruption technique for integrated process development strategies. Biotechnology Progress, 24(3), 606-614.
[16] Freimund, S., Sauter, M., Kappeli, O. & Dutler, H.(2003). A new non-degrading isolation process for 1,3- β-D-glucan of high purity from baker’s yeast Saccharomyces cerevisiae. Carbohydrate Polymers. 54, 159–171.
[17] Box, G.E.P. & Draper, N.R. (2007). Response Surface Mixtures and Ridge Analysis. John Wiley and Sons, New Jersey.
[18] Khuiri, A.I. & Cornell, J.A. (1996). Response Surfaces: Desings and Analysis, Marcel Dekker, New York.
[19] Zadeh, L.A. (1965). Fuzzy sets. Information Control, 338-353.
[20] Bashiri, M. & Hosseininezhad, S.J. (2009). A Fuzzy Programming for Optimizing Multi Response Surface in Robust Designs. Journal of Uncertain Systems, 3(3), 163-173.
[21] Bashiri, M. & Hosseininezhad, S.J. (2012). Fuzzy Development of Multiple Response Optimization. Group Decision and Negotiation, 21(3), 417-438.
[22] Türkşen, Ö . & Apaydın, A. (2014). A Modeling Approach Based on Fuzzy Least Squares Method for Multi-Response Experiments with Replicated Measures. Chaos, Complexity and Leadership 2012 Springer Proceedings in Complexity, Springer Netherlands, 153-158.
[23] Türkşen, Ö . & Güler, N. (2015). Comparison of fuzzy logic based models for the multi-response surface problems with replicated response measures. Applied Soft Computing, 37, 887-896.
[24] Türkşen, Ö . (2016). Analysis of Response Surface Model Parameters with Bayesian Approach and Fuzzy Approach. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 24(1), 109−122.
[25] Türkşen, Ö . (2018). A Fuzzy Modeling Approach for Replicated Response Measures Based on Fuzzification of Replications with Descriptive Statistics and Golden Ratio. Süleyman Demirel University Journal of Natural and Applied Sciences, 22(1), 153-159.
[26] Mendel, J.M. (2017). Type-1 Fuzzy Sets and Fuzzy Logic. Uncertain Rule-Based Fuzzy Systems, Springer International Publishing AG, 25-99.
[27] Lai, Y.J. Hwang, C.L. (1992). Fuzzy Mathematical Programming. Springer Verlag, Berlin.