Lin Jer-An, Kuo Chia-Hung, Chen Bao-Yuan, Li Ying, Liu Yung-Chuan, Chen Jiann-Hwa, Shieh Chwen-Jen
Biotechnology Center, National Chung Hsing University, 250 Kuo-kuang Road, Taichung 40227, Taiwan.
Department of Seafood Science, National Kaohsiung Marine University, 142 Haijhuan Road, Nanzih District, Kaohsiung 81157, Taiwan.
Ultrason Sonochem. 2016 Sep;32:258-264. doi: 10.1016/j.ultsonch.2016.03.018. Epub 2016 Mar 19.
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.
白藜芦醇是一种很有前景的具有多种生物功能的植物化学物质，在虎杖中含量丰富。已经报道了几种白藜芦醇的提取方法，但这些方法通常提取时间长且提取率低。在本研究中，开发了一种新型酶辅助超声法从虎杖中高效提取白藜芦醇。结果表明，在提取过程中应用糖苷酶（果胶酶或复合酶）后，白藜芦醇的产量显著增加，并且与复合酶辅助提取相比，果胶酶辅助提取具有更好的提取效果。随后，选择了一种具有响应面法（RSM）和人工神经网络（ANN）的五水平四因素中心复合旋转设计来对白藜芦醇辅助超声提取进行建模和优化。根据从设计数据计算出的决定系数（R²），与RSM模型相比，ANN模型在数据拟合方面显示出更高的准确性。ANN模型确定的最佳提取条件为底物浓度5%、声功率150W、pH值5.4、温度55℃、酶与底物的比例为3950聚半乳糖醛酸酶单位（PGNU）/g虎杖以及反应时间5小时，这可导致白藜芦醇产量显著提高，达到11.88mg/g。
