An application of fuzzy linear modeling: prediction of uncertainty for beta-glucan content

Özlem Türkşen, S. Ertunç
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Abstract

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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模糊线性模型在β -葡聚糖含量不确定度预测中的应用
-葡聚糖(BG)对哺乳动物的健康有积极的影响。然而,BG来源的内容有限。此外,BG的生产程序严格,生产率低。天然气的经济生产需要改进天然气的生产步骤。在本研究中,通过对添加剂(EDTA、CaCl2和山梨糖醇)的优化,旨在提高生产BG的第一步,即微生物生长步骤中BG的含量。为此,在EDTA、CaCl2和山梨醇的特定水平下,获得了具有重复响应测量(RRM)的实验数据集。模糊建模是一种灵活的建模方法,由于实验数据集规模小,且难以满足概率建模假设。根据模糊最小二乘法得到预测模糊函数。为了得到EDTA、CaCl2和山梨醇的最优值,基于多目标优化(MOO)方法对预测模糊函数进行最大化。利用EDTA、CaCl2和山梨糖醇的最优值,从经济角度评价预测BG含量的不确定性。
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来源期刊
CiteScore
3.30
自引率
6.20%
发文量
13
审稿时长
16 weeks
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