Ultrasonic-Assisted Extraction of Flavonoids from <i>Juglans mandshurica</i> Maxim.: Artificial Intelligence-Based Optimization, Kinetics Estimation, and Antioxidant Potential

Abstract

Ultrasonic-assisted extraction (UAE) of flavonoids (JMBF) from Juglans mandshurica Maxim., an important industrial crop in China, was investigated in the present study. To improve the extraction efficiency of JMBF, suitable UAE was proposed after optimization using a hybrid response surface methodology–artificial neural network–genetic algorithm approach (RSM–ANN–GA). The maximum extraction yield (6.28 mg·g−1) of JMBF was achieved using the following optimum UAE conditions: ethanol concentration, 62%; solid–liquid ratio, 1:20 g·mL−1; ultrasonic power, 228 W; extraction temperature, 60 °C; extraction time, 40 min; total number of extractions, 1. Through the investigation of extraction kinetics, UAE offered a higher saturated concentration (Cs) for JMBF in comparison to traditional solvent extraction (TSE). Scanning electron microscopy (SEM) images showed that deeper holes were generated in J. mandshurica powder under the action of ultrasound, indicating that ultrasound significantly changed the structure of the plant materials to facilitate the dissolution of active substances. Extracts obtained using UAE and TSE were compared by Fourier-transform infrared spectroscopy analysis, the results of which revealed that the functional group of bioactive compounds in the extract was unaffected by the ultrasonication process. Moreover, JMBF was further shown to exhibit significant antioxidant properties in vitro. This study provides a basis for the application of JMBF as a natural antioxidant.

Keywords

• ultrasonic-assisted extraction
• <i>Juglans mandshurica</i> Maxim.
• flavonoids
• artificial neural network
• antioxidant