利用BP神经网络模型预测卵形沙眼冷冻贮藏期

Q1 Agricultural and Biological Sciences
Weiqing Lan , Xin Yang , Taoshuo Gong , Jing Xie
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引用次数: 10

摘要

本研究旨在利用反向传播(BP)神经网络模型建立不同冷冻温度下卵形Trachinotus ovatus的保质期预测模型。在266 K、255 K、243 K、233 K和218 K的温度下测量pH、总挥发性碱性氮(TVB-N)、硫代巴比妥酸(TBA)、保水性(保水能力[WHC];蒸煮损失)和感官评价。选择266K、255K、233K和218K条件下的TVB-N和保水性结果,建立BP神经网络模型,并在243K条件下验证该模型。结果表明,低温延缓了卵管的pH值、TVB-N值和TBA值的升高,提高了卵管保水能力。BP神经网络模型具有较高的回归系数(r2:0.642–0.9904),较低的均方误差(MES:0.1658–1.7882),相对误差在10%以内,能够准确预测在266 K–218 K的冷冻温度下卵形Trachinotus ovatus的质量变化。因此,(BP)神经网络模型在预测卵黄颡鱼冷冻保存期方面具有很大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting the shelf life of Trachinotus ovatus during frozen storage using a back propagation (BP) neural network model

The research aimed to create a shelf life prediction model for Trachinotus ovatus in different freezing temperatures by using back propagation (BP) neural network model. The pH, total volatile basic nitrogen (TVB-N), thiobarbituric acid (TBA), water retention (water holding capacity [WHC]; cooking loss), and sensory evaluation were measured under 266 K, 255 K, 243 K, 233 K, and 218 K temperatures. The results of TVB-N and water retention during 266 K, 255 K, 233 K, and 218 K conditions were selected to build a BP neural network model and verify the model at 243 K. Results indicated that low temperatures retarded the rise of pH, TVB-N, and TBA values, improving water retention capacity of Trachinotus ovatus. The BP neural network model had high regression coefficients (r2: 0.8642–0.9904), low mean square error (MES: 0.1658–1.7882), and relative error within 10% and could accurately predict the quality change of Trachinotus ovatus under the freezing temperatures of 266 K–218 K. Therefore, (BP) neural network model has great potential in predicting the shelf life of Trachinotus ovatus in frozen storage.

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来源期刊
Aquaculture and Fisheries
Aquaculture and Fisheries Agricultural and Biological Sciences-Aquatic Science
CiteScore
7.50
自引率
0.00%
发文量
54
审稿时长
48 days
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