| 牛妍1，苗钧魁2，刘小芳2，王西西2，冷凯良2，杨天燕1.基于响应面法和人工神经网络优化
甘油三酯型鱼油制备工艺[J].中国油脂,2026,51(6):.[NIU Yan1,MIAO Junkui2,LIU Xiaofang2,WANG Xixi2,
LENG Kailiang2, YANG Tianyan1.Optimization of triglyceride-form fish oil process based on response surface methodology and artificial neural network[J].China Oils and Fats,2026,51(6):.] |
| 基于响应面法和人工神经网络优化
甘油三酯型鱼油制备工艺 |
| Optimization of triglyceride-form fish oil process based on response surface methodology and artificial neural network |
|
投稿时间：2025-05-16 修订日期：2026-02-11 录用日期：2025-06-20
出版日期：2026-06-20
|
| DOI：10.19902/j.cnki.zgyz.1003-7969.250244 |
| 中文关键词: 鱼油 甘油三酯 工艺优化 响应面法 人工神经网络 |
| 英文关键词:fish oil triglyceride process optimization response surface methodology artificial neural network |
| 基金项目:山东省海洋养殖创新创业共同体项目（YZ2024001） |
|
| Author Name | Affiliation | | NIU Yan1,MIAO Junkui2,LIU Xiaofang2,WANG Xixi2,
LENG Kailiang2, YANG Tianyan1 | 1.College of Fisheries,Zhejiang Ocean University, Zhoushan 316022, Zhejiang, China
2.Yellow Sea
Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, Shandong, China |
|
| 摘要点击次数: 473 |
| 全文下载次数: 12 |
| 中文摘要: |
| 旨在为高含量甘油三酯型鱼油的酶法工业化生产提供参考，以乙酯型鱼油和甘油为底物，采用酶催化酯交换反应制备高含量甘油三酯型鱼油，通过单因素试验确定振荡速度、反应温度、反应时间及加酶量对酯交换反应的影响。在此基础上，采用Box-Behnken设计酯交换反应工艺条件，并结合响应面法（RSM）和人工神经网络（ANN）模型分别对工艺进行优化，通过比较筛选出适宜的工艺优化方法，同时考察了脂肪酶的重复利用性能。结果表明：相较于ANN模型，RSM模型拟合程度更高，实测值与预测值之间误差更小，更适合作为高含量甘油三酯型鱼油工艺优化方法；通过RSM模型确定的最优工艺条件为振荡速度180 r/min、反应温度65 ℃、反应时间24 h、加酶量2%（以乙酯型鱼油质量计），在此条件下甘油三酯含量为76.10%；脂肪酶重复使用7次仍能达到工艺要求（甘油三酯含量≥76%）。综上，RSM模型可避免因数据量不足导致模型预测能力受限的问题，对于优化甘油三酯型鱼油制备工艺具有较强的准确性和适用性。 |
| 英文摘要: |
| Aiming to provide a reference for the industrial enzymatic production of high-content triglyceride-form fish oil, ethyl ester-form fish oil and glycerol were used as substrates and enzymatic transesterification was employed to prepare high-content triglyceride-form fish oil. Single-factor experiments were conducted to determine the effects of oscillation speed, reaction temperature, reaction time, and enzyme dosage on the transesterification reaction. Based on this, the transesterification conditions were designed using the Box-Behnken method, and both response surface methodology (RSM) and artificial neural network (ANN) models were applied to optimize the process respectively. By comparing the two approaches, a suitable process optimization method was selected. Additionally, the reusability of the lipase was investigated. The results indicated that compared to ANN model, RSM model exhibited a higher degree of fit and smaller errors between measured and predicted values, making it more suitable for optimizing the process of high-content triglyceride-form fish oil. The optimal process conditions selected through the RSM model were as follows: oscillation speed 180 r/min, reaction temperature 65 ℃, reaction time 24 h, and enzyme dosage 2% (based on the mass of ethyl ester-form fish oil). Under these conditions, the triglyceride content reached 76.10%. The lipase could meet the process requirements(the triglyceride content≥76%) after being reused seven times. In conclusion, the RSM model avoids the limitation of predictive capability due to insufficient data volume and demonstrates strong accuracy and applicability for optimizing the preparation process of triglyceride-form fish oil. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|
