An application of fuzzy linear modeling: prediction of uncertainty for beta-glucan content
DOI:
https://doi.org/10.11121/ijocta.01.2019.00664Keywords:
Beta-Glucan, Yeast, Fuzzy Least Squares, Triangular Type-1 Fuzzy Numbers, Interval EstimationAbstract
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.
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