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建立并应用含两种优势背景菌的新鲜猪肉中 生长预测动力学模型。

Establishment and Application of a Predictive Growth Kinetic Model of with the Appearance of Two Other Dominant Background Bacteria in Fresh Pork.

机构信息

Laboratory of Pathogenic Microorganisms Inspection, Livestock and Poultry Products Quality & Safety Risk Assessment Laboratory (Qingdao) of MARA, China Animal Health and Epidemiology Center, Qingdao 266032, China.

College of Food Science and Engineering, Ocean University of China, Qingdao 266003, China.

出版信息

Molecules. 2022 Nov 8;27(22):7673. doi: 10.3390/molecules27227673.

Abstract

To better guide microbial risk management and control, growth kinetic models of with the coexistence of two other dominant background bacteria in pork were constructed. Sterilized pork cutlets were inoculated with a cocktail of Derby ( Derby), (), and Escherichia coli (), and incubated at various temperatures (4-37 °C). The predictive growth models were developed based on the observed growth data. By comparing of primary models, Baranyi models were preferred to fit the growth curves of Derby and , while the Huang model was preferred for (all ≥ 0.997). The secondary Ratkowsky square root model can well describe the relationship between temperature and (all ≥ 0.97) or (all ≥ 0.98). Growth models were validated by the actual test values, with and close to 1, and around 0.001. The time for Derby to reach a pathogenic dose (10 CFU/g) at each temperature in pork was predicted accordingly and found to be earlier than the time when the pork began to be judged nearly fresh according to the sensory indicators. Therefore, the predictive microbiology model can be applied to more accurately predict the shelf life of pork to secure its quality and safety.

摘要

为了更好地指导微生物风险管理和控制,构建了与猪肉中另外两种优势背景菌共存的 生长动力学模型。将含有德比( Derby)、单核细胞增生李斯特菌()和大肠杆菌()的混合物接种到已消毒的猪肉片上,并在不同温度(4-37°C)下孵育。根据观察到的生长数据,建立了预测生长模型。通过比较主要模型的,巴尼模型更适合拟合德比和 的生长曲线,而黄模型更适合拟合 (所有 ≥ 0.997)。二次罗特卡斯基平方根模型可以很好地描述温度与 (所有 ≥ 0.97)或 (所有 ≥ 0.98)之间的关系。通过实际测试值验证了生长模型,其 值接近 1, 值约为 0.001。根据预测模型,预测了每种温度下猪肉中德比达到致病剂量(10 CFU/g)所需的时间,发现这一时间早于根据感官指标判断猪肉开始接近新鲜的时间。因此,预测微生物学模型可以更准确地预测猪肉的保质期,以确保其质量和安全。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adf2/9696609/47d3f2499ea0/molecules-27-07673-g001.jpg

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