食品科学 ›› 2020, Vol. 41 ›› Issue (9): 139-144.doi: 10.7506/spkx1002-6630-20190404-058

• 包装贮运 • 上一篇    下一篇

大西洋鲑贮藏过程中微生物生长预测系统的构建

于祝祝,林洪,王静雪   

  1. (中国海洋大学食品科学与工程学院,山东 青岛 266003)
  • 出版日期:2020-05-15 发布日期:2020-05-15
  • 基金资助:
    “十三五”国家重点研发计划重点专项(2016YFD0400105);现代农业产业技术体系建设专项(CARS-47)

Predictive System of Microbial Growth on Atlantic Salmon during Storage

YU Zhuzhu, LIN Hong, WANG Jingxue   

  1. (College of Food Science and Engineering, Ocean University of China, Qingdao 266003, China)
  • Online:2020-05-15 Published:2020-05-15

摘要: 菌落总数、假单胞菌数量和希瓦氏菌数量是影响大西洋鲑新鲜度的重要指标,因此,探明上述微生物的变化情况将有利于监测和预判贮运过程中大西洋鲑的品质。本研究通过修正的Gompertz模型、Baranyi and Roberts模型以及Belehradek方程拟合大西洋鲑在不同贮藏温度下的菌落总数、假单胞菌数量和希瓦氏菌数量的变化情况,得到大西洋鲑贮藏过程中微生物数量的变化规律,并运用Visual Basic语言编写生成微生物生长预测系统。结果表明:通过比较修正的Gompertz模型和Baranyi and Roberts模型拟合所得的4、10、25 ℃贮藏温度下微生物生长参数发现,Baranyi and Roberts模型更适宜作为一级模型反映大西洋鲑贮藏过程中微生物数量随时间的变化情况;运用该模型拟合大西洋鲑在不同贮藏温度下菌落总数、假单胞菌数量和希瓦氏菌数量所得的决定系数(R2)均大于0.9,其中25 ℃下分别为0.995、0.994、0.952,10 ℃下分别为0.993、0.996、0.997,4 ℃下分别为0.981、0.995、0.914。根据Baranyi and Roberts模型拟合所得参数利用Belehradek方程拟合微生物生长延滞时间、最大比生长速率与贮藏温度的关系,并在此基础上通过Visual Basic语言编写生成微生物生长预测软件,快速获得了不同贮藏温度下微生物的数量及生长曲线,为预测和监控大西洋鲑中微生物生长提供一种高效、便捷的工具。

关键词: 大西洋鲑, 微生物, 预测系统, 假单胞菌, 希瓦氏菌

Abstract: The total viable count and the number of Pseudomonas and Shewanella are important indicators affecting the freshness of Atlantic salmon. Therefore, the detection of their changes can help monitor and pre-judge the quality of Atlantic salmon during storage and transportation. The total viable count and the number of Pseudomonas and Shewanella in Atlantic salmon during storage at different temperatures were nonlinearly fitted to the modified Gompertz, Baranyi and Roberts and Belehradek models. Results showed that the Baranyi and Roberts model was more suitable to reflect the changes in the number of microorganisms on Atlantic salmon during storage at 4, 10, and 25 ℃ when compared with the modified Gompertz model. The coefficients of determination (R2) of the Baranyi and Roberts models for the total viable count and the number of Pseudomonas and Shewanella were greater than 0.9, 0.995, 0.994 and 0.952 at 25 ℃, 0.993, 0.996 and 0.997 at 10 ℃, and 0.981, 0.995 and 0.914 at 4 ℃, respectively. The lag period of the growth curve and the maximum specific growth rate versus storage temperature were fitted to the Bellehradek equation using parameters obtained from the Baranyi and Roberts models. Furthermore, a microbial growth prediction software was written in Visual Basic (VB) to obtain the number and growth curve of microorganisms at different storage temperatures quickly, which will provide an efficient and convenient tool for pre-judging and monitoring microbial growth in Atlantic salmon.

Key words: Atlantic salmon, microorganisms, prediction system, Pseudomonas, Shewanella

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