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绿色提取工艺优化:采用响应面法(RSM)和遗传算法-人工神经网络(GA-ANN)模型从……中提取天然抗氧化活性成分。 (原文中“from”后面缺少具体内容)

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

作者信息

Li Jingnan, Bao Haiying, Huo Huimin

机构信息

College of Chinese Medicinal Materials, Jilin Agricultural University, Changchun, China.

College of Traditional Chinese Medicine and Key Laboratory of Edible Fungi Resources and Utilization, Ministry of Agriculture and Rural Affairs, Jilin Agricultural University, Changchun, China.

出版信息

Prep Biochem Biotechnol. 2025 Jul 31:1-15. doi: 10.1080/10826068.2025.2533448.

Abstract

(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 and its industrial applications.

摘要

(牛肝菌属)卡尔斯特牛肝菌是一种珍稀药用真菌,富含甾醇和多酚,以其天然抗氧化特性而闻名。为提高其价值,开发了一种可持续提取技术,在相同条件下比较了振荡提取、热水提取和超声辅助提取(UAE)的产率。结果表明,超声辅助提取明显优于其他方法。采用响应面法(RSM)和遗传算法-人工神经网络(GA-ANN)模型对超声辅助提取工艺进行优化,以最大化目标成分的提取率。与响应面法相比,遗传算法-人工神经网络模型表现出卓越的预测和优化性能。确定的最佳条件为超声时间120分钟、液料比22:1毫升/克、乙醇浓度50%和超声功率63瓦。优化后,甾醇和多酚的提取率分别比初始方法提高了171.92%和69.56%。抗氧化测试进一步表明,优化后的提取物具有更强的自由基清除能力。这些发现为卡尔斯特牛肝菌的高效可持续开发及其工业应用提供了理论基础。

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