Ultrasound-Assisted Extraction of Antioxidant Compounds from Pomegranate Peels and Simultaneous Machine Learning Optimization Study

Abstract

The pomegranate, a widely consumed fruit, produces large quantities of waste, mainly from its peel. Pomegranate peels (PPs) contain high amounts of antioxidant compounds, such as polyphenols, flavonoids, and anthocyanins, which can be isolated from them and used for the benefit of humans and the environment. In the present work, a study of recovery of these compounds by ultrasound-assisted extraction (UAE) was carried out, whose parameters were optimized. The optimal results were a total polyphenol content of 195.55 mg gallic acid equivalents/g, total flavonoid content of 74.78 mg rutin equivalents/g, total anthocyanin content of 992.87 μg cyanidin 3-O-glucoside equivalents/g, and ascorbic acid content of 15.68 mg/g, while the antioxidant activity determined through ferric-reducing antioxidant power and DPPH assays was 2366.89 and 1755.17 μmol ascorbic acid equivalents/g, respectively. In parallel, an artificial intelligence (AI)-based framework was developed to model and predict antioxidant and phytochemical responses from UAE parameters. Six machine learning models were implemented on the experimental dataset, with the Random Forest (RF) regressor consistently achieving the best predictive accuracy. Partial dependence analysis revealed ethanol concentration as the dominant factor influencing outcomes, while ultrasonic power and extraction time exerted comparatively minor effects. Although dataset size limited model generalizability, the RF model reproduced experimental outcomes within experimental variability, underscoring its suitability for predictive extraction optimization. These findings demonstrate the complementary role of machine learning in accelerating antioxidant compound recovery research and its potential to guide future industrial-scale applications of AI-assisted extraction.