高水分南美白对虾虾干货架期预测模型的构建  

Construction of Shelf-life Predictive Model for Dried Penaeus vannamei with High Moisture

作　　者：宣晓婷 陈思媛 乐耀元 尚海涛 曾昊溟 凌建刚 张文媛 XUAN Xiaoting;CHEN Siyuan;LE Yaoyuan;SHANG Haitao;ZENG Haoming;LING Jian'gang;ZHANG Wenyuan(Institute of Agricultural Products Processing,Ningbo Academy of Agricultural Sciences,Ningbo,Zhejiang 315040,China;Zhenhai High School,Ningbo,Zhejiang 315040,China)

机构地区：[1]宁波市农业科学研究院农产品加工研究所,浙江宁波315040 [2]镇海中学,浙江宁波315040

出　　处：《农产品加工》2022年第19期78-82,90,共6页Farm Products Processing

基　　金：宁波市公益类科技计划项目(202002N3077);国家重点研发计划项目(2016YFD0400304-03)。

摘　　要：以真空冷冻干燥获得的高水分南美白对虾虾干(水分含量30%~40%)为材料,研究不同贮藏温度(4,7,20,30℃)对高水分南美白对虾贮藏过程中品质的影响,且建立基于Arrhenius方程的动力学模型和基于综合指标和关键指标的BP神经网络模型。通过模拟剩余货架期试验,动力学模型平均误差为13.52%,预测精度为86.48%;基于关键指标的BP神经网络模型平均误差为9.67%,预测精度为90.33%;基于综合指标的BP神经网络模型平均误差为5.86%,预测精度为94.14%。因此,在综合指标的BP神经网络基础上的高水分南美白对虾货架期预测模型能更加贴切地预测货假期。The effects of different storage temperatures(4,7,20,30℃)on the quality of high-moisture Penaeus vannamei dried by vacuum freeze-drying(moisture content 30%~40%)were studied.The dynamic model based on Arrhenius equation and BP neural network model based on comprehensive index and key index were established.The average error of kinetic model was 13.52%and the prediction accuracy was 86.48%by simulating the remaining shelf life.The average error of BP neural network model based on key indicators was 9.67%,and the prediction accuracy was 90.33%.The average error of BP neural network model based on comprehensive indicators was 5.86%,and the prediction accuracy was 94.14%.Therefore,the shelf life prediction model of high moisture Penaeus vannamei based on BP neural network could better predict the shelf-life.

关 键 词：南美白对虾 高水分 BP神经网络模型 Arrhenius方程 

分 类 号：TS201.1[轻工技术与工程—食品科学]