RSM和模糊建模法优化芦笋抗氧化成分的提取

Akshay K. Singh, Vivek Kumar, Karunakar Singh, Sumaiya Fatima
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引用次数: 0

摘要

本研究旨在探讨不同提取方法对从芦笋中提取抗氧化剂的影响。与连续振荡萃取(CSE)法相比,微波辅助萃取(MAE)法在外消旋藻根乙醇提取物中的总酚含量(TPC)、总黄酮含量(TFC)、2,2-二苯基-1-苦基肼(DPPH)、铁还原抗氧化能力(FRAP)和总抗氧化能力(TAC)含量方面更为有效。响应面法(RSM)的Box-Behnken实验设计用于优化三个工艺参数的MAE方法,即溶剂浓度(SC:40%-90%v/v)、萃取时间(ET:10-30 最小值)和微波功率(MP:360–720 W) 。通过多元回归分析和方差分析确定实验数据的模型拟合度。最佳目标参数为65%v/v SC,23 最小ET和560 W-MP,相应的TPC、TFC、DPPH、FRAP和TAC反应为9.377 mg GAE/g,177.03 mg QE/g,65.70%,193.35 mM Fe+2/g和496.97 mg AAE/g,期望值为0.95。利用Mamdani型模糊推理的模糊建模,在一组工艺参数和响应规则下,获得了指定工艺参数和反应的最优条件。RSM和模糊模型对响应的估计结果与实验值吻合较好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

RSM and fuzzy modeling approach for optimization of extraction of antioxidant compounds from Asparagus racemosus roots

RSM and fuzzy modeling approach for optimization of extraction of antioxidant compounds from Asparagus racemosus roots

This research work was carried out to explore the influence of extraction methods on the extraction of antioxidant from Asparagus racemosus roots. The microwave-assisted extraction (MAE) method was more efficient as compared with continuous shaking extraction (CSE) method in terms of total phenolic content (TPC), total flavonoids content (TFC), 2,2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing antioxidant power (FRAP), and total antioxidant capacity (TAC) content in the ethanolic extract of A. racemosus roots. Box–Behnken experimental design of response surface methodology (RSM) was used to optimize MAE method for three process parameters viz. solvent concentration (SC: 40%–90% v/v), extraction time (ET: 10–30 min), and microwave power (MP: 360–720 W). Model fitness of the experimental data was determined by multiple regression analysis and analysis of variance. The optimum targeted parameters were obtained as 65% v/v SC, 23 min ET, and 560 W MP, and the corresponding responses TPC, TFC, DPPH, FRAP, and TAC were found to be 9.377 mg GAE/g, 177.03 mg QE/g, 65.70%, 193.35 mM Fe+2/g, and 496.97 mg AAE/g with a desirability of 0.95. Fuzzy modeling with Mamdani-type fuzzy inference was also utilized to obtain the most optimal conditions for specified process parameters and responses under the set of rules for process parameters and responses. The estimated results of responses by both RSM and fuzzy model showed good agreement with the experimental values.

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