OPTIMIZATION AND CHARACTERIZATION OF WOOD DECAY MUSHROOM Ganoderma adspersum EXTRACT: A COMPARISON BETWEEN RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORK-ANT LION ALGORITHM

Abstract

In this study, the bioactive properties of Ganoderma adspersum, a wood-decaying mushroom, were investigated. The study was designed in three steps: an experimental study, optimization of extraction conditions, and determination of bioactive properties of the optimum extracts. The main research problem was to determine the most effective extraction conditions to maximize the bioactive potential of G. adspersum using advanced optimization techniques. The extraction conditions were designed according to the I-optimal design and optimized using both the response surface method and the integration of artificial neural networks-ant lion algorithm. In the third step of the study, the bioactive properties of the two estimated extraction conditions and the extraction condition providing the highest total antioxidant status value obtained from the experimental studies were evaluated. Antioxidant activity, total phenolic and flavonoid content, antimicrobial properties, anticholinesterase activity, and phenolic content of three different optimum extracts were determined. As a result, the optimum extraction conditions suggested by artificial neural networks-ant lion algorithm optimization showed the best overall bioactive activity, highlighting the effectiveness of hybrid artificial intelligence-based models in bioactive compound extraction processes.