摘要
为提高南瓜多糖得率,采用超声辅助提取法,从液料比、超声功率和超声时间3个因素进行单因素实验,并基于Box-Behnken设计分别应用响应面法和遗传算法-BP神经网络模型对提取工艺进行优化。结果表明,遗传算法-BP神经网络和响应面法优化的最佳工艺条件下,多糖实际得率与预测值的相对误差分别为0.75%和0.96%,遗传算法-BP神经网络具有更好的预测能力。优化后的最佳提取参数为液料比311、超声功率252 W、超声时间10 min,在此条件下多糖得率为25.17%,为南瓜资源的开发与利用提供了参考依据。
To enhance the yield of pumpkin polysaccharides,this study employed an ultrasound-assisted extraction method and conducted single-factor experiments focusing on three variables:liquid-to-material ratio,ultrasonic power,and ultrasonic time.Based on the Box-Behnken design,both response surface methodology(RSM)and a genetic algorithm-backpropagation neural network(GA-BP neural network)model were applied to optimize the extraction process.The results showed that under the optimal conditions predicted by the GA-BP neural network and RSM,the relative errors between the actual and predicted yields were 0.75%and 0.96%,respectively,indicating that the GA-BP neural network exhibited superior predictive performance.The optimized extraction parameters were a liquid-to-material ratio of 311,ultrasonic power of 252 W,and extraction time of 10 minutes,under which the polysaccharide yield reached 25.17%.This study provides a valuable reference for the development and utilization of pumpkin resources.
作者
李新胜
杨璐
荣爽
王慧竹
LI Xinsheng;YANG Lu;RONG Shuang;WANG Huizhu(School of Chemistry Pharmaceutical Engineering,Jilin University of Chemical Technology,Jilin City 132022,China)
出处
《吉林化工学院学报》
2025年第7期4-10,共7页
Journal of Jilin Institute of Chemical Technology
关键词
南瓜多糖
超声
遗传算法
BP神经网络
pumpkin polysaccharide
ultrasonic
genetic algorithm
neural network
