Deep eutectic solvent-based green extraction of Strychnos potatorum seed phenolics: Process optimization via response...

Area of Science:

• Green Chemistry
• Natural Product Extraction
• Biotechnology

Background:

• Strychnos potatorum seeds (SPs) are a source of bioactive compounds.
• Traditional extraction methods may lack efficiency and environmental friendliness.
• Deep eutectic solvents (DES) offer a sustainable alternative for natural product extraction.

Purpose of the Study:

• To optimize the extraction of bioactive compounds from SPs using a glycerol-sodium acetate DES.
• To compare the predictive accuracy of Response Surface Methodology (RSM) and Artificial Neural Networking (ANN) for extraction optimization.
• To evaluate the antioxidant and antidiabetic potential of the SPs extracts.

Main Methods:

• Extraction optimization using a glycerol-sodium acetate DES.
• Independent variables: shaking time, temperature, solvent-to-feed ratio.
• Optimization and prediction using RSM and ANN models.
• Evaluation of extraction yield, total phenolic content (TPC), total flavonoid content (TFC), antioxidant activity (DPPH), and α-amylase inhibitory activity.

Main Results:

• Optimal extraction conditions determined: 29 min, 40°C, 30 mL/g.
• Extracts showed high TPC (30.43 mg GAE/g DW), TFC (10.99 mg RE/g DW), antioxidant activity (26.16%), and α-amylase inhibition (46.95%).
• ANN demonstrated lower percentage error and absolute average deviation (AAD) compared to RSM, indicating higher predictive accuracy.
• Strong correlation observed between TPC and TFC.

Conclusions:

• Glycerol-sodium acetate DES is an efficient and eco-friendly solvent for extracting bioactive compounds from SPs.
• ANN is a superior predictive model for optimizing extraction processes compared to RSM.
• The SPs extracts possess significant antioxidant and antidiabetic properties.