Dergiler / An International Journal of Optimization and Control: Theories & Applications (IJOCTA) / 2019 / Cilt: 9 - Sayı: 3

An application of fuzzy linear modeling: prediction of uncertainty for betaglucan content

Sayfa
45–51
Yayın Tarihi
2022-10-11
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

By using the optimal values of EDTA, CaCl2 and Sorbitol, theuncertainty for predicted BG content is evaluated from the economic perspective

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Beta-glucan (BG) has positive health effects for the mamalians.
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Temel Bulgular & Veri:In this study, it is aimed to improve theBG 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.
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Sonuç & Katkı:By using the optimal values of EDTA, CaCl2 and Sorbitol, theuncertainty for predicted BG content is evaluated from the economic perspective
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

Beta-glucan (BG) has positive health effects for the mamalians. However, the BGsources have limited content of it. Besides, the production of the BG has stringentprocedures with low productivity. Economical production of the BG needs theimprovement of the BG production steps. In this study, it is aimed to improve theBG 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. Fuzzymodeling, a flexible modeling approach, is applied on the experimental data setbecause of the small sized data set and diffulty of satisfying probabilistic modelingassumptions. The predicted fuzzy function is obtained according to the fuzzy leastsquares 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, theuncertainty for predicted BG content is evaluated from the economic perspective

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Bu Makaleye Atıf Yap

ERTUNÇ, S., TÜRKŞEN, Ö. (2022). An application of fuzzy linear modeling: prediction of uncertainty for betaglucan content. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), Cilt: 9 - Sayı: 3, 45–51. https://ajindex.com/articles/440596

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Kaynakça (13)

  1. Mantovani, M.S., Bellini, M.F., Angeli, J.P.F., Oliveira, R.J., Silva, A.F. & Ribeiro, L.R. (2008). -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, Mori M.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 -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). -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 the yeast 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). βglucan 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.