Green extraction process optimization: extracting natural antioxidant active ingredients from Inonotus hispidus using RSM and GA-ANN modeling

Inonotus hispidus (Bull.) P. Karst., a rare medicinal fungus, is rich in sterols and polyphenols, recognized for their natural antioxidant properties. To enhance its value, a sustainable extraction technique was developed, comparing the yields of Orbital Shaking Extraction, Hot Water Extraction, and Ultrasound-Assisted Extraction (UAE) under identical conditions. The results showed that UAE significantly outperformed the other methods. Response Surface Methodology (RSM) and Genetic Algorithm-Artificial Neural Network (GA-ANN) models were employed for optimizing the UAE process to maximize the extraction rates of the desired components. The GA-ANN model demonstrated superior predictive and optimization performance compared to RSM. The optimal conditions identified were an ultrasound time of 120 min, a liquid-to-material ratio of 22:1 mL/g, an ethanol concentration of 50%, and ultrasonic power of 63 W. Following optimization, the extraction rates of sterols and polyphenols increased by 171.92% and 69.56%, respectively, compared to the initial method. Antioxidant tests further indicated that the optimized extract exhibited enhanced free radical scavenging capabilities. These findings provide a theoretical foundation for the efficient and sustainable development of Inonotus hispidus and its industrial applications.