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Food Science and Biotechnology
→ Food Science and Biotechnology 2021 ; 30(1): 37-45
Simultaneous optimization of ultrasound-assisted extraction for total flavonoid content and antioxidant activity of the tender stem of Triarrhena lutarioriparia using response surface methodology
Qingming Cao1 • Jianye Yan2,4 • Zhicheng Sun3 • Limin Gong4 • Hongnian Wu4 • Shihan Tan4 • Yating Lei4 • Bo Jiang5 • Yuanqing Wang1
1 College of Food and Life Science, Central South University of Forestry and Technology, Changsha, China 2 Science and Technology Innovation Center, Hunan University of Chinese Medicine, Changsha, China 3 Department of Spine Surgery, Xiangya Hospital of Central South University, Changsha, China 4 School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China 5 Hunan Center for Drug Evaluation, Certification and ADR Monitoring, Hunan Food and Drug Administration, Changsha, China
ABSTRACT
The asparagus of Triarrhena lutarioriparia (TL) is a popular vegetable with abundant chemical compounds in China. This study aims to optimize the ultrasound-assisted extraction (UAE) method for its content of total flavonoid and antioxidant activities by response surface methodology (RSM). Box-Behnken design was adopted to evaluate the influences of ethanol concentration, extraction time and solvent-to-sample ratio on the extraction yield of total flavonoid and the antioxidant activity. Considering the maximum content of extracted total flavonoids and antioxidant activity, the optimal extraction conditions were acquired with 70% (v/v) ethanol by UAE for 60 min at a solvent-to-sample ratio of 40 mL/g. The proportion of the extraction of total flavonoid was 15.88 mg/g and antioxidant activity reached 79.53%. The RSM would be recommended as an appropriate model for simultaneous optimization of the UAE conditions for the content of total flavonoid and the antioxidant activity of asparagus of TL.
KEYWORD
Asparagus of Triarrhena lutarioriparia · Ultrasound-assisted extraction · Total flavonoid · Antioxidant activity · Response surface methodology
Food Science and Biotechnology 2021 ; 30(1): 37-45
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