Response Surface Methodology to Optimize Supercritical Carbon Dioxide Extraction of Polygonum cuspidatum. (19th August 2021)

• Record Type: Journal Article
• Authors: Ruan, Ningjie; Jiao, Zhen; Tang, Linglong
• Is Part Of: Journal of AOAC International. Volume 105:Number 1(2022)
• Page Start: 272
• Page End: 281
• Publication Date: 2021-08-19
• Subjects: Agricultural chemistry -- Periodicals; Food -- Analysis -- Periodicals; 543
• DOI: 10.1093/jaoacint/qsab108
• Languages: English
• ISSNs: 1060-3271

Abstract

Abstract: Background: Resveratrol and emodin are abundant polyhydroxy compounds that are found in the Chinese traditional medicinal herb Polygonum cuspidatum and widely used due to their excellent antioxidant properties. Objective: This study aimed to obtain the maximum extraction yields of resveratrol and emodin from P. cuspidatum via green extraction. Methods: The extracts were acquired through supercritical carbon dioxide (SC-CO2 ) extraction with ethanol as cosolvent. The independent variables of the extraction process, namely temperature (45–55°C), pressure (20–30 MPa), and ethanol content (80–120 mL/L), were optimized by response surface methodology. Results: These variables had marked effects on the yields of resveratrol and emodin. Second-order polynomial mathematical models were developed and applied to predict the optimal extraction conditions (i.e., temperature of 51.8°C, pressure of 25.34 MPa, and ethanol content of 110.83 mL/L). Under these conditions, confirmatory experiments showed that the yields of resveratrol and emodin were 2.564 ± 0.121 and 2.804 ± 0.108 mg/g, respectively. High antioxidant properties, strong free radical scavenging abilities, and good reducing abilities were observed for the extracts. Conclusion: Resveratrol and emodin can be successfully extracted from P. cuspidatum by SC-CO2, and RSM can optimize the process. Highlights: The SC-CO2 extraction of resveratrol and emodin from P. cuspidatum was developed, and RSM was successfully used to optimize the extraction parameters and predict the optimal conditions.