Optimization of ultrasound-assisted extraction of phenolic compounds from grapefruit (Citrus paradisi Macf.) leaves via D-optimal design and artificial neural network design with categorical and quantitative variables

Abstract

BACKGROUND: The extraction of phenolic compounds from grapefruit leaves assisted by ultrasound-assisted extraction (UAE) was optimized using response surface methodology (RSM) by means of D-optimal experimental design and artificial neural network (ANN). For this purpose, five numerical factors were selected: ethanol concentration (0-50%), extraction time (15-60 min), extraction temperature (25-50 degrees C), solid:liquid ratio (50 - 100 gL(-1)) and calorimetric energy density of ultrasound (0.25-0.50 kW L-1), whereas ultrasound probe horn diameter (13 or 19 mm) was chosen as categorical factor.