Green Ultrasound‐Assisted Extraction of Bioactive Phenolics From Quince (Cydonia oblonga) Leaves: Multi‐Response Optimization, LC–MS/MS Profiling, and Comparison With Conventional Methods

ABSTRACT Quince ( Cydonia oblonga Mill.) leaves are an abundant yet underutilized agricultural by‐product that is rich in phenolic compounds and therefore represents a promising feedstock for value‐added bioactive extracts. This study optimized the green ultrasound‐assisted extraction (UAE) of bioactive compounds from quince leaves using a sequential statistical approach based on Plackett–Burman screening and Taguchi‐based criteria importance through intercriteria correlation (CRITIC)‐weighted grey relational analysis (GRA). Under the optimum conditions (solid‐to‐liquid ratio 3:50 g/mL, 50% (v/v) ethanol, and 75°C), the extract exhibited a total phenolic content (TPC) of 9210.44 ± 138.25 mg GAE L − 1 , a total flavonoid content (TFC) of 6180.67 ± 94.25 mg CE L − 1 and a DPPH free radical‐scavenging capacity of 76.53 ± 3.65 mmol TE L − 1 . Triplicate confirmation experiments validated the model, with an overall prediction accuracy of 97.69%. Ethanol concentration exerted the greatest influence on the grey relational grade (36.28%). Under identical operating conditions, the optimized UAE provided the most balanced extraction performance. It yielded the highest TPC, antioxidant activity, anti‐inflammatory activity, and α‐amylase inhibition relative to maceration, magnetic stirring, hand‐shaking, and reflux, while LC–MS/MS identified quinic and chlorogenic acids as the principal constituents. These results indicate that the UAE optimized through a Taguchi‐based CRITIC‐weighted GRA is an effective and sustainable strategy for producing bioactive‐rich extracts from quince‐leaf waste. Practical Applications This study provides a practical, statistically grounded route for converting quince ( Cydonia oblonga Mill.) leaf waste, an abundant agro‐industrial by‐product, into phenolic‐rich extracts using a green, food‐grade ultrasound‐assisted process. The sequential screening and multi‐response optimization framework can be adopted by food, nutraceutical, and functional‐ingredient manufacturers to maximize antioxidant, anti‐inflammatory, and enzyme‐inhibitory activities while minimizing solvent and energy use, supporting cost‐effective and sustainable valorization of leaf biomass.