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OA北大核心CSCDCSTPCD

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.