食品与发酵工业2023，Vol.49Issue(24)：148-155,8.DOI:10.13995/j.cnki.11-1802/ts.036912
基于神经网络和响应面法对比优化富硒绿豆芽蛋白提取工艺研究
Comparison and optimization of protein extraction from selenium-enriched mung bean sprouts based on an artificial neural network and a response surface method
摘要
Abstract
To explore the extraction process of selenium-rich mung bean sprout protein,this study evaluated the performance of the constructed response surface methodology(RSM)and artificial neural network(ANN)models from multiple perspectives.The amino acid content and organic selenium form of selenium-rich mung bean sprout protein were determined.The experimental results showed that com-pared with RSM,the ANN model had a higher fitting correlation coefficient,smaller error value,and more accurate prediction perform-ance.The optimal extraction process was as follows:lye concentration of0.15 mol/L,extraction temperature of 43 ℃,extraction time of 133 min,and liquid-solid ratio of 50∶1(mL∶g).Under this condition,the protein extraction rate was 90.32％,and the yield was 62.23％after three times of extraction.The total amino acid content and essential amino acid content of Se-rich mung bean sprouts protein were 49.89 g/100 g and 19.92 g/100 g,among which glutamic acid content was the highest(7.85 g/100 g),which met the ideal pro-tein condition proposed by FAO/WHO.Besides,it was found that selenomethionine(SeMet)was the main binding mode between amino acids and selenium in selenium-rich mung bean sprouts.This study hoped to provide a theoretical reference for developing and applying functional Se-enriched foods.关键词
富硒豆芽/响应面/神经网络/遗传算法Key words
selenium-enriched bean sprouts/response surface/neural network/genetic algorithm引用本文复制引用
王露露,明佳佳,杨涛,徐晨凤,肖园园,张驰,邓伶俐,商龙臣..基于神经网络和响应面法对比优化富硒绿豆芽蛋白提取工艺研究[J].食品与发酵工业,2023,49(24):148-155,8.基金项目
湖北省教育厅科学研究计划资助项目(D20221902) （D20221902）
湖北省自然科学基金青年项目(2023AFB162) （2023AFB162）
湖北民族大学高水平科研成果校内培育项目(PY22009) （PY22009）
湖北民族大学生物与食品工程学院研究生创新项目(SGYC2022013) （SGYC2022013）
