Maximization of bioactive potential of Fomes fomentarius via artificial Intelligence-Assisted optimization

In this study, in order to maximize the biological activity of the medicinal mushroom species Fomes fomentarius, extraction parameters were evaluated by Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA) hybrid optimization. The effects of extraction temperature, time and ethanol/water ratio on total antioxidant capacity (TAC) were modeled. As a result of the study, the optimum conditions were determined as 41.080 °C, 41.751 min and 49.149% ethanol/water ratio for RSM. For ANN-GA, the optimum conditions were determined as 39.848 °C, 39.689 min and 88.490% ethanol/water ratio. Extracts obtained under optimal conditions were compared in terms of antioxidant (FRAP, DPPH, TAS, TOS, OSI), anticholinesterase (AChE, BChE), antiproliferative (A549, MCF-7, DU-145) activities, and LC-MS/MS phenolic compound profiles. RSM extracts exhibited higher activities than ANN-GA in most parameters. Particularly high values were obtained for FRAP, DPPH, phenolic compound concentrations, and antiproliferative activity. The results demonstrated that the optimization approach was decisive in the bioactive compound profile and pharmacological potential of the extract. These results support the position of F. fomentarius as an important natural resource that can be used in pharmaceutical and functional food fields.