Machine learning-aided enhancement of white tea extraction efficiency using hybridized GMDH models in microwave-assisted extraction

Abstract

White tea is valuable for having a high antioxidant content, which is considered to possess numerous beneficial effects on health. This study investigated the application of microwave-assisted extraction (MAE) for the extraction of total phenolic compounds from white tea. The experimental setup included four independent variables: microwave power (ranging from 100 to 300 W), extraction time (ranging from 10 to 40 min), temperature (ranging from 35 to 50 °C), and the ratio of food to solvent (ranging from 0.25 to 0.5 g/10 mL). The responses that were evaluated were IC50 (ppm) and total phenolic content (mg/g). The experimental design consisted of thirty runs conducted within the MAE system. The group method of data handling (GMDH) models were used to predict important efficiency measures (IC50 and total phenol content) in the extraction process. The models were assessed based on their ability to capture the relationships between input conditions and efficiency outputs. Three GMDH variants were compared: baseline GMDH, GMDH optimized with a genetic algorithm (GMDH-GA), and GMDH optimized with a harmony search algorithm (GMDH-HS). While all models achieved high predictive ability on a test set, GMDH-HS emerged as the superior performer. It achieved near-perfect agreement with observations (d-index > 0.998), minimal errors (NRMSE < 0.02), and effectively captured data variance (NSE > 0.99) for both outputs. Correlation diagrams and Taylor diagrams confirmed the superior performance of GMDH-HS in terms of linearity, correlation, and error minimization. This study demonstrates the effectiveness of hybridizing GMDH with a harmony search algorithm for complex modeling tasks, paving the way for improved efficiency and yield optimization in extraction processes.