Bioactive-rich extracts from Aeginetia indica flowers: Statistical optimization of extraction conditions

Aeginetia indica flowers are an underutilized botanical resource with potential health benefits, yet their phytochemical and antioxidant profiles remain poorly characterized. This study aimed to optimize the extraction of total phenolic content (TPC), total flavonoid content (TFC), and antioxidant capacities assessed by DPPH and FRAP assays from dried A. indica flowers using response surface methodology combined with central composite design. Four independent variables, including ethanol concentration (20–100 %, v/v), solvent-to-solid ratio (20–60 mL/g), extraction time (1–3 h), and extraction temperature (30–70 °C), were systematically evaluated to determine their individual and interactive effects on the extraction outcomes. The results showed that ethanol concentration and solvent-to-solid ratio significantly influenced all responses ( p < 0.0001), with pronounced linear, quadratic, and interaction effects. Optimal extraction conditions (52 % ethanol concentration, 50 mL/g solvent-to-solid ratio, 2.5 h extraction time, and 60 °C extraction temperature) yielded high experimental values for TPC (61.33 mg GAE/g), TFC (442.83 mg RE/g), DPPH (125.42 mg TE/g), and FRAP (187.55 mg TE/g). Experimental validation confirmed the model’s robustness and predictive accuracy ( R 2 > 0.98, C.V.% < 3.5, non-significant lack-of-fit). The application of a desirability function enabled simultaneous optimization of all responses, demonstrating the effectiveness of multivariate process design. This study presents a scalable and food-compatible extraction protocol for maximizing antioxidant-rich phytochemicals from A. indica flowers. The findings highlight the potential of this ethnomedicinal plant for incorporation into functional foods, nutraceuticals, and natural health products, promoting its valorization in sustainable bioresource development. • Extraction of bioactive compounds from Aeginetia indica flowers was optimized. • RSM-CCD effectively modeled and improved the extraction conditions. • Ethanol concentration and solvent ratio were key factors influencing yields. • Optimal extraction used 52 % ethanol, 50 mL/g ratio, 2.5 h, and 60 °C. • Models demonstrated strong predictive accuracy with R 2 values above 0.98.