Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/94858
標題: A novel enzyme-assisted ultrasonic approach for highly efficient extraction of resveratrol from Polygonum cuspidatum
作者: Lin, Jer-An
Kuo, Chia-Hung
Chen, Bao-Yuan
Li, Ying
Liu, Yung-Chuan
Chen, Jiann-Hwa
Shieh, Chwen-Jen
關鍵字: Artificial neural network;Enzyme-assisted ultrasonic approach;Polygonum cuspidatum;Response surface methodology;Resveratrol;Fallopia japonica;Stilbenes;Temperature;Ultrasonics
Project: Ultrasonics sonochemistry, Volume 32, Page(s) 258-264.
摘要: 
Resveratrol is a promising multi-biofunctional phytochemical, which is abundant in Polygonum cuspidatum. Several methods for resveratrol extraction have been reported, while they often take a long extraction time accompanying with poor extraction yield. In this study, a novel enzyme-assisted ultrasonic approach for highly efficient extraction of resveratrol from P. cuspidatum was developed. According to results, the resveratrol yield significantly increased after glycosidases (Pectinex® or Viscozyme®) were applied in the process of extraction, and better extraction efficacy was found in the Pectinex®-assisted extraction compared to Viscozyme®-assisted extraction. Following, a 5-level-4-factor central composite rotatable design with response surface methodology (RSM) and artificial neural network (ANN) was selected to model and optimize the Pectinex®-assisted ultrasonic extraction. Based on the coefficient of determination (R(2)) calculated from the design data, ANN model displayed much more accurate in data fitting as compared to RSM model. The optimum conditions for the extraction determined by ANN model were substrate concentration of 5%, acoustic power of 150W, pH of 5.4, temperature of 55°C, the ratio of enzyme to substrate of 3950 polygalacturonase units (PGNU)/g of P. cuspidatum, and reaction time of 5h, which can lead to a significantly high resveratrol yield of 11.88mg/g.
URI: http://hdl.handle.net/11455/94858
DOI: 10.1016/j.ultsonch.2016.03.018
Appears in Collections:生物科技發展中心

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