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动力学建模与优化白屈菜(Galium mollugo L.)地上部分树脂状物质的浸渍和超声提取

Kinetic modeling and optimization of maceration and ultrasound-extraction of resinoid from the aerial parts of white lady's bedstraw (Galium mollugo L.).

机构信息

High Chemical and Technological School for Professional Studies, Kruševac, Serbia.

出版信息

Ultrason Sonochem. 2013 Jan;20(1):525-34. doi: 10.1016/j.ultsonch.2012.07.017. Epub 2012 Aug 3.

Abstract

In this paper, extraction of resinoid from the aerial parts of white lady's bedstraw (Galium mollugo L.) using an aqueous ethanol solution (50% by volume) was studied at different temperatures in the absence and the presence of ultrasound. This study indicated that ultrasound-assisted extraction was effective for extracting the resinoid and gave better resinoid yields at lower extraction temperature and in much shorter time than the maceration. A phenomenological model was developed for modeling the kinetics of the extraction process. The model successfully describes the two-step extraction consisting of washing followed by diffusion of extractable substances and shows that ultrasound influences only the first step. The extraction process was optimized using response surface methodology (RMS) and artificial neural network (ANN) models. For the former modeling, the second-order polynomial equation was applied, while the second one was performed by an ANN-GA combination. The high coefficient of determination and the low MRPD between the ANN prediction and the corresponding experimental data proved that modeling the extraction process in the absence and the presence of ultrasound using ANN was more accurate than RSM modeling. The optimum extraction temperature was determined to be 80 and 40 °C, respectively for the maceration and the ultrasound-assisted extraction, ensuring the highest resinoid yield of 22.0 g/100g in 4h and 25.1g/100g in 30 min, which agreed with the yields obtained experimentally for the same time (21.7 and 25.3g/100g, respectively).

摘要

本文研究了在无超声和有超声条件下,用体积分数为 50%的乙醇水溶液从白车轴草(Galium mollugo L.)地上部分中提取树脂的过程。结果表明,超声辅助提取法是提取树脂的有效方法,与浸渍法相比,在较低的提取温度和更短的时间内,可获得更高的树脂收率。建立了描述提取过程动力学的经验模型。该模型成功地描述了由洗涤和可提取物的扩散组成的两步提取过程,表明超声仅影响第一步。利用响应面法(RMS)和人工神经网络(ANN)模型对提取过程进行了优化。对于前者的建模,应用了二阶多项式方程,而后者则通过 ANN-GA 组合进行。高决定系数和 ANN 预测与相应实验数据之间的低 MRPD 证明,使用 ANN 对无超声和有超声条件下的提取过程进行建模比 RMS 建模更准确。确定的最佳提取温度分别为 80°C 和 40°C,用于浸渍法和超声辅助提取法,可在 4 小时和 30 分钟内分别获得最高的树脂收率 22.0 g/100g 和 25.1 g/100g,这与相同时间内实验获得的收率(分别为 21.7 和 25.3 g/100g)一致。

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