南美白对虾货架期预测指标选择及模型研究
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(1. 上海海洋大学信息学院,上海 201306;2. 农业部渔业信息重点实验室,上海 201306)

作者简介:

黄幸幸,女,上海海洋大学在读硕士研究生。

通讯作者:

葛艳(1974—),女,上海海洋大学副教授,博士。E-mail: yge@shou.edu.cn

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基金项目:

上海市科技创新行动计划项目(编号:16391902902);江苏省国家长江珍稀鱼类工程技术研究中心培育点(编号:BM2013012)


The prediction index and model of the shelf-life of Penaeus Vannamei
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(1. College of Information, Shanghai Ocean University, Shanghai 201306, China; 2. Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai 201306, China)

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    摘要:

    为研究南美白对虾品质指标与货架期之间的关系及南美白对虾在贮藏过程中的品质变化过程,精确预测其剩余货架期,通过检测277,272.2,255 K温度下南美白对虾的感官指标、理化指标和微生物指标,分别针对南美白对虾品质检测的综合指标和部分关键指标,以支持向量机模型和BP神经网络模型为基础,建立南美白对虾货架期预测模型。结果表明:基于综合指标构建的货架期预测模型的预测精度(支持向量机为97.71%,BP为91.41%)比基于关键指标的(支持向量机为84.08%,BP为83.76%)高;基于支持向量机的预测模型的预测精度(关键指标为84.08%,综合指标为97.71%)比BP预测模型的(关键指标为83.76%,综合指标为91.41%)高;基于综合指标的支持向量机预测模型的预测精度是4种模型中最高的,为97.71%。该结论也可为支持向量机方法和预测指标选择在其他食品领域货架期的应用研究提供一定的参考。

    Abstract:

    In order to precisely predict the remaining shelf life of Penaeus Vannamei, the relationship between quality indexes and remaining shelf life and the quality change process of it during the storage process were studied. The sensory and physical-chemical indexes, and microorganisms of P. Vannamei at 277 K, 272.2 K and 255 K were first tested in this study. Then, the prediction models of the shelf life of P. vannamei were established for the comprehensive and some key indexes of its quality, and this were based on both the support vector machine and the BP neural network models. The results showed that the prediction accuracies of the shelf-life prediction models based on the comprehensive indexes of P. Vannamei (97.71% for SVM model and 91.41% for BP model) were higher than those of the prediction models based on several key indexes (84.08% for SVM model and 83.76% for BP model). Meanwhile, the prediction accuracies of the prediction models based on support vector machine (84.08% for key indexes and 97.71% for comprehensive indexes) were higher than those of BP prediction models (83.76% for key indexes and 91.41% for comprehensive indexes). Moreover, the prediction accuracy of the support vector machine (SVM) model based on the comprehensive indexes was 97.71%, which were the highest among the four models. The conclusion also provided a reference for the application of support vector machine and selection of prediction indexes in the shelf-life of other food fields.

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黄幸幸,陈明,葛艳,等.南美白对虾货架期预测指标选择及模型研究[J].食品与机械,2017,33(4):105-109,116.
HUANGXingxing, CHENMing, GEYan, et al. The prediction index and model of the shelf-life of Penaeus Vannamei[J]. Food & Machinery,2017,33(4):105-109,116.

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  • 在线发布日期: 2023-03-10
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