Optimization of bioactives extraction from Polygonum cuspidatum and application of enzyme catalysis

• Main Authors: Bao-Yuan Chen, 陳抱元
• Other Authors: Jian-Hua Chen
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/qr6272

碩士 === 國立中興大學 === 分子生物學研究所 === 101 === Polygonum cuspidatum is a traditional Chinese herbal medicine. The roots of P. cuspidatum have been used in treatments for arthralgia, chronic bronchitis, hypertension, hypercholesterolemia, trauma and inflammation. The known compounds of resveratrol, piceid, and emodin are mainly constituent in the roots extracts of P. cuspidatum. The pharmacological activities of P. cuspidatum are mostly associated with these compounds. Resveratrol and piceid have been shown to carry various biological activities such as antioxidant capacity, anti-inflammatory, anti-cancer, and cardioprotective activities, whereas emodin has been shown to possess pharmaceutical properties such as anti-inflammatory, antibacterial, and anti-tumor activities. In view of P. cuspidatum with variety of biological activities, the studies of extraction bioactivity compounds and biotransformation process were focus in this research. It could be divided into three parts described as below. Part 1: Ultrasonic-assisted extraction of piceid, resveratrol, and emodin from Polygonum cuspidatum was performed. In order to extract the bioactive compounds from plants, ultrasonic-assisted extraction technique was employed in this part. The ultrasound could create cavitations in the liquid solution to increase the efficiency of extraction. Besides, the mass transfer effects were evaluated for comparing the extraction efficiency of ultrasonic-assisted extraction with shaking extraction. The effect of temperature, ethanol concentration and ultrasound power on extraction yield of piceid, resveratrol, and emodin were evaluated by response surface methodology using a 3-variables-3-levels box-behnken design. Thus, the extraction conditions could be optimized for each compound.   Part 2: Enzymatic transformation of piceid to form aglycone resveratrol using β-glucosidase was studied. Generally, resveratrol is generally present in its glycoside form in nature plants. Piceid is a major glycoside form derivatives of resveratrol. The content of piceid is usually higher than its aglycone, resveratrol. However, the bioavailability of piceid is lower than resveratrol. Therefore, it is important to develop a technique to transform piceid into resveratrol by deglycosylation for improving its bioavailability. In order to achieve this purpose, a commercial enzyme ViscozymeR L was employed for transforming piceid to resveratrol in this part. The main object of this part was to optimize the enzymatic transformation conditions. The effect of temperature, time, enzyme amount and pH was examined by using response surface methodology with a central composite design. Furthermore, the optimal condition was applied to transform piceid in the water extracts of Polygonum cuspidatum roots to resveratrol. Part 3: Optimization of enzymatic-assisted extraction of resveratrol from Polygonum cuspidatum by using response surface methodology(RSM) and artificial neural network (ANN) was studied. The principle of enzymatic-assisted extraction was utilized glycosidase to degrade or break down the plant cell wall for enhancement of the target component released from plant sources in extraction process. Comparing with traditional extraction method, enzymatic-assisted extraction not only decreased the solvent usage and energy consumption, but also increased the extraction yield of bioactive compounds. A commercial glycosidase complex, ViscozymeR L, was employed for enzymatic-assisted extraction process in order to increase the extraction yield of resveratrol. In addition, the effect on extraction yield of resveratrol with or without enzyme treatment was compared. The effect of temperature, time, enzyme amount and pH was examined by using 4-variables-5-levels central composite experiment design which was combined with response surface methodology or artificial neural network. The fitness of response surface methodology and artificial neural network was also compared. Finally, the optimal extraction conditions could be attained by using suitable prediction model.