外文摘要：This study aimed to optimize the accelerated solvent extraction (ASE) condition of zeaxanthin from orange paprika using a response surface methodology (RSM) or an artificial neural network (ANN) with a genetic algorithm (GA). Input variables were ethanol concentration, extraction time, and extraction temperature, while output variable was zeaxanthin. The mean squared error and regression correlation coefficient of the developed ANN model were 0.3038 and 0.9983, respectively. Predicted optimal extraction conditions from ANN-GA for maximum zeaxanthin were 100% ethanol, 3.4 min, and 99.2 degrees C. The relative errors under the optimal extraction conditions were RSM for 10.46% and ANN-GA for 2.18%. We showed that the recovery of hydrophobic zeaxanthin could be performed using ethanol, an eco-friendly solvent, via ASE, and the extraction efficiency could be improved by ANN-GA modeling than RSM. Therefore, combining ASE and ANN-GA might be desirable for the efficient and eco-friendly extraction of hydrophobic functional materials from food resources.
外文关键词：optimization;artificial neural network;genetic algorithm;Accelerated solvent extraction;Orange paprika;zeaxanthin
作者：Kim, Jaecheol;Lee, Ga Eun;Kim, Suna
作者单位：Seoul Natl Univ;Korea Natl Open Univ;Sungshin Womens Univ
期刊名称：FOOD SCIENCE AND BIOTECHNOLOGY
期刊影响因子：0.0
出版年份：2024
出版刊次：33（11）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台