Phenolic content and biological activities of Lenzites betulina extracts obtained by ultrasonic-assisted optimization approaches

In this study, an extraction process was developed using ultrasonic-assisted optimization methods to enhance the biological activities of the medicinally important mushroom Lenzites betulina. Two different approaches were applied to optimize the extraction parameters: Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). These methods were used to determine the optimal extraction conditions by modeling the interactions between key variables such as temperature, time, and solvent ratio. Extracts obtained under optimal conditions were comprehensively evaluated by analyzing antioxidant, anticholinesterase, antiproliferative, and phenolic content. The findings indicated that extracts optimized using the RSM model exhibited higher phenolic compound concentrations and superior biological activities, including antioxidant capacity, enzyme inhibition potential, and cell-based antiproliferative effects, compared to those obtained using the ANN-GA model, primarily due to the improved preservation of phenolic structural integrity under RSM conditions. These results indicate that L. betulina is a biologically rich natural resource and its therapeutic potential can be increased by properly optimizing the extraction parameters.