Optimization of ultrasound-assisted extraction and biological activities of Crataegus monogyna Jacq. flowering branches using experimental design and artificial neural networks

Crataegus monogyna is widely used in traditional medicine due to its rich content of bioactive compounds. The present study aimed to evaluate the influence of extraction parameters, including solvent concentration, extraction time, temperature, and solid-to-liquid ratio, on total phenolics, total flavonoids, antioxidant DPPH (2,2-diphenyl-1-picrylhydrazyl), ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid), FRAP (ferric reducing antioxidant power), and CUPRAC (cupric reducing antioxidant capacity)), and enzyme inhibitory properties (α-amylase and tyrosinase). Furthermore, an Artificial Neural Network (ANN) model was applied to optimize the extraction conditions. The results demonstrated that extraction conditions significantly affect both chemical composition and biological activity. The optimal conditions (70% ethanol, 30 min, 45 °C, (1 : 30)) yielded extracts with high total phenolic (109.15 mg GAE per g), and total flavonoid content (70.74 mg RE per g), strong antioxidant activity (DPPH: 473.57 mg TE per g, ABTS: 537.58 mg TE per g, CUPRAC: 407.11 mg TE per g, FRAP: 393.91 mg TE per g) and notable enzyme inhibitory potential (0.53 mmol ACAE per g against α-amylase and 75.00 mg KAE per g against tyrosinase). The ANN model showed excellent predictive performance, confirming its suitability for modeling and optimization of complex extraction systems. The present study provides comprehensive insights into optimized ultrasound-assisted extraction methods for the recovery of maximum bioactive compounds and biological activities (antioxidant and enzyme inhibition effect) from C. monogyna flowering branches using experimental design and ANN model. These findings provide a reliable basis for the development of functional and pharmaceutical products. This journal is