Phenolics extraction from sweet potato peels: modelling and optimization by response surface modelling and artificial...

Area of Science:

• Food Science and Technology
• Natural Product Chemistry
• Biotechnology

Background:

• Sweet potato peels (SPP) are an abundant agricultural waste with low commercial value.
• Phenolic compounds in SPP possess significant free radical scavenging activity, indicating potential health benefits.
• Valorization of SPP can lead to sustainable food industry practices and novel product development.

Purpose of the Study:

• To investigate the aqueous extraction of phenolics from SPP.
• To model and optimize extraction conditions using Response Surface Methodology (RSM) and Artificial Neural Network (ANN).
• To compare the efficacy of RSM and ANN in predicting optimal phenolic yield.

Main Methods:

• Aqueous extraction of phenolics from SPP.
• Central Composite Design (CCD) for experimental design.
• Optimization using Response Surface Methodology (RSM) and Artificial Neural Network (ANN).
• Assays for Total Phenolic Content (TPC), ABTS, and DPPH radical scavenging activity.

Main Results:

• Temperature and solvent-to-solid ratio significantly influenced TPC, ABTS, and DPPH values positively.
• Extraction time negatively affected ABTS assay results.
• Both RSM and ANN models predicted optimal conditions at 60 mL/g ratio, 30 min, and 75°C.
• Optimized extraction yielded 11.87 mg GAE/g DM for TPC, 12.91 mg TE/g DM for ABTS, and 46.35 mg TE/g DM for DPPH.

Conclusions:

• Optimized aqueous extraction effectively recovers valuable phenolics from SPP.
• RSM and ANN are reliable tools for modeling and optimizing phenolic compound extraction.
• The findings present an opportunity for the food industry to utilize SPP for high-value, health-promoting products.