Ultrasound-assisted extraction of polyphenols from avocado residues: Modeling and optimization using response surface methodology and artificial neural networks

Abstract

Seed and peel avocado (Persea Americana) are agro-industrial residues whose structure presents an important quantity of source of polyphenolic components which can be obtained by various extraction methods. Response surface methodology (RSM) and the artificial neural network (ANN) were used to model and optimize the conditions of ultrasound-assisted extraction (UAE) (25 W/L) with respect to temperature (40 - 60 °C), concentration of ethanol/water (30% - 60%) and extraction time (40 - 80 min) in obtaining phenolic from avocado residues. RSM and ANN allowed finding an optimal phenolic content for seeds (145.170 - 146.569 mg GAE/g; 49 °C, 41.2% and 65.5 - 65.1 min) and peels (124.050 - 125.187 mg GAE/g; 50.9 °C, 49.5% and 61.8 min). The models estimated between predicted and experimental values were significant (p < 0.05), presenting a high correlation (R2> 0.9907) and a low root mean square error for the prediction of phenolics (RMSE < 0.9437 mg GAE/g). The results of this study allow the design of efficient, economic and ecologically friendly extraction procedures in the industry for obtaining bioactive metabolites from avocado residues.