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Synthetic biology applied in the agrifood sector: Public perceptions, attitudes and implications for future studies

Background

The genus Chaenomeles Lindley (Chaenomeles), known as Mugua (in Chinese), is a medicinal and edible plant that is considered a functional source for human health promoting. Numerous bio-, chemical- and physical technologies have been applied to explore the relationship between the phytochemistry of Chaenomeles and their bioactivities, opening the door to their potential application in the field of function food and traditional Chinese medicine.

Scope and approach

This paper aimed to systematically summarize available information on Chaenomeles and provide reference for further investigations. The extracted and isolated chemical constituents (e.g., triterpenoids, sesquiterpenoids, flavonoids, organic acids, phenolic compounds, volatile oil and fatty acids) from five species of Chaenomeles were mainly summarized. This review highlights the theoretical aspects and recent developments of nonconventional extraction (microwave, ultrasound and enzyme-assisted extraction, super- and subcritical fluid extraction and solid phase extraction) technologies for bioactive compounds recovery from Chaenomeles. Additionally, scientific reports on crude extracts or pure compounds from this plant revealed broad bioactivities, including antioxidant, antimicrobial, antiinflammatory activities, as well as immunoregulatory and hepatoprotective effects.

Key findings and conclusions

Approximately 150 chemicals have been characterized from different parts of Chaenomeles. Triterpenoids, flavonoids and phenolic compounds are the major bioactive ingredients of this plant. Additionally, the crude extracts and monomer compounds have been demonstrated with various in vitro and in vivo pharmacological functions. Nonconventional extraction technologies have been proposed to provide efficient, systematic, and practical protocols for the sustainable valorization of Chaenomeles.

» Author: Shan Jin, Beth Clark, Sharron Kuznesof, Xuan Lin, Lynn J. Frewer

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This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement Nº 690103

               

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