Optimization of Enzyme–Ultrasound-Assisted Extraction of Almond ( Amygdalus communis L.) Phenolics using Response Surface Methodology and Deep Neural Networks, and Their In-Vitro Bioactivities

High Resolution Image Download MS PowerPoint Slide This study optimized the extraction of bioactive compounds from almond ( Amygdalus communis L.), a medicinal and edible resource from Xinjiang, using ultrasound-assisted enzymatic extraction (pectinase: cellulase). Response surface methodology (RSM) and a deep neural network (DNN) were applied to evaluate ethanol concentration, ultrasound time, solid–liquid ratio, and enzyme dosage on total polyphenol (TPC) and flavonoid content (TFC). Both models predicted extraction outcomes effectively, but RSM ( R 2 = 0.9509) outperformed DNN ( R 2 = 0.6844) with lower relative error (0.63% vs 0.73%). Optimal RSM conditions were ethanol concentration 65%, ultrasound time 31 min, solid–liquid ratio 29 g/mL, and enzyme dosage 2 mg/g. Under these parameters, guanosine, protocatechuic acid, caffeic acid, narcissin, and taxifolin contents reached 287.78 ± 1.93, 49.01 ± 0.82, 17.35 ± 0.88, 231.41 ± 3.85, and 5.90 ± 0.18 μg/g, respectively. Extracts showed strong antioxidant activity, with maximum DPPH, ABTS, • OH, and O 2 •– radical scavenging rates of 87.21%, 74.57%, 66.42%, and 69.03%. They also inhibited α-amylase (80.86%), α-glucosidase (82.27%), and pancreatic lipase (72.73%), suggesting hypoglycemic and lipid-lowering potential. However, concentrations above 250 μg/mL were cytotoxic to RAW264.7 cells. This work offers a feasible strategy for almond bioactive utilization.