海洋科学2025，Vol.49Issue(12)：115-127,13.DOI:10.11759/hykx20260202001
基于响应面和人工神经网络的紫菜中类菌孢素氨基酸提取工艺优化
Optimization of the extraction process of mycosporine-like amino acids from Pyropia yezoensis using response surface methodology and artificial neural networks
摘要
Abstract
Mycosporine-like amino acids(MAAs)are a class of organic compounds that have been demonstrated to exhibit UV-protective,antioxidant,and anti-inflammatory properties.They have remarkable applications in the cosmetics,food,and pharmaceutical industries.A multitude of seaweed species have been found to contain MAAs,with red alga Pyropia yezoensis(P.yezoensis)being a particularly noteworthy example because of its remarkably high MAA content.The present study employed response surface methodology and artificial neural network analy-sis to optimize the MAA extraction process from P.yezoensis.Based on single-factor experimental investigations,response surface and artificial neural network models were designed and constructed using the MAA extraction rate as an indicator,followed by a comparative analysis and validation of the two modeling approaches.These findings suggest that both models consistently demonstrate that the solid-liquid ratio and extraction time exert the most considerable influence on the extraction rate.The response surface model exhibited outstanding predictive capabil-ity and reliability,with the experimental extraction rate achieved under optimized conditions(0.468%)closely matching the predicted value(0.474%).Although the artificial neural network model demonstrated high good-ness-of-fit(R²>0.93)on the training and validation sets,it exhibited substantial prediction errors on the independ-ent test set.This finding suggests that the model exhibits inferior generalization capability in comparison with the response surface model.The optimal extraction conditions were determined to be as follows:temperature:42℃,time duration:1.51 h,solid-to-liquid ratio:1:25(g/mL),extraction cycles:2 cycles,and methanol concentration:20%.Under these conditions,the extraction yield was found to be 0.468%.This study provides a valuable reference point for the large-scale extraction and utilization of MAAs from P.yezoensis.关键词
类菌孢素氨基酸/条斑紫菜/提取工艺/响应面/人工神经网络Key words
Mycosporine-like amino acids/Pyropia yezoensis/extraction process/response surface/artificial neural network分类
海洋科学引用本文复制引用
张静,王立军,羌玺,黄丹琳,解修俊,王旭雷,马增岭,王广策..基于响应面和人工神经网络的紫菜中类菌孢素氨基酸提取工艺优化[J].海洋科学,2025,49(12):115-127,13.基金项目
山东省重点研发计划(重大科技创新工程)项目(2025CXGC010618,2025LZGC037) （重大科技创新工程）
国家自然科学基金(42276146,41876124) （42276146,41876124）
浙江省自然科学基金会(No.LZ21C030001) （No.LZ21C030001）
农业农村部(CARS-50) （CARS-50）
青岛市关键技术攻关项目(25-1-1-gjgg-54-hy) （25-1-1-gjgg-54-hy）
山东省"泰山学者"工程专项经费资助项目(tspd20210316)Key R&D Program of Shandong Province,China,Nos.2025CXGC010618,2025LZGC037 （tspd20210316）
National Natural Science Foundation of China,Nos.42276146,41876124 （）
the Zhejiang Provincial Natural Science Foundation of China,No.LZ21C030001 （）
Ministry of Agriculture and Rural Affairs of the People's Republic of China,No.CARS-50 （）
Key Technology Research Projects in Qingdao City,China,No.25-1-1-gjgg-54-hy （）
the Research Fund for the Taishan Scholar Project of Shandong Province,No.tspd20210316 （）
