U.S. Dept. of Agriculture, Agricultural Research Service, Eastern Regional Research Center, 600 East Mermaid Lane, Wyndmoor, PA, 19038, USA.
Department of Food Science & Technology, Univ. of Georgia, Athens, GA, 30602, USA.
J Food Sci. 2019 Mar;84(3):590-598. doi: 10.1111/1750-3841.14448. Epub 2019 Feb 7.
A model was developed to predict the growth of Bacillus cereus from spores during cooling of cooked pasta. Cooked pasta was inoculated with a cocktail of four strains of heat-shocked (80 °C/10 min) B. cereus spores to obtain a final spore concentration of approximately 2 log CFU/g. Thereafter, growth was determined at isothermal temperatures starting at 10 °C and every three degrees up to 49 °C. Samples were removed periodically and plated on mannitol egg yolk polymyxin agar. The plates were incubated for 24 hr at 30 °C. Baranyi, Huang, and modified Gompertz primary growth models were used to fit growth data. The modified Ratkowsky secondary model was used to fit growth rates determined by the primary growth models with respect to temperature. All three primary models fitted the growth data well. The modified Ratkowsky secondary model adequately fit growth rates generated by the three primary models (R values ranging from 0.96 to 0.98). After acceptable prediction zone (APZ) validation and goodness of fit statistical analyses, it was determined that the Baranyi primary growth model was best suited for these data. For both single-rate exponential cooling and biphasic linear cooling model validation, all Baranyi model predictions (n = 24 and 28, respectively) fell within the APZ (-1.0 to 0.5 log CFU/g). The model will assist institutional food service settings to determine the safety of cooked pasta subjected to longer cooling times or stored at improper temperatures. PRACTICAL APPLICATION: Predictive model can be used to estimate extent of microbial growth during cooling of cooked pasta and in designing HACCP program and setting of critical control levels. Retail food industry would need fewer challenge studies to validate the safety of their products. The model will provide regulatory agencies and food industry with an objective means of assessing the microbial risk and ensuring that the public is not at risk of acquiring food poisoning.
建立了一个模型来预测冷却煮熟的面食过程中芽孢杆菌的生长。将热休克（80°C/10 分钟）的四个芽孢杆菌菌株的混合物接种到煮熟的面食中，以获得大约 2 个对数 CFU/g 的最终孢子浓度。此后，在 10°C 起始的等温温度下确定生长情况，每隔 3°C 升高到 49°C。定期取出样品并在甘露醇卵黄多粘菌素琼脂平板上进行平板计数。平板在 30°C 下孵育 24 小时。使用 Baranyi、Huang 和改进的 Gompertz 初始生长模型拟合生长数据。使用改进的 Ratkowsky 二次模型拟合通过三个初始模型确定的与温度相关的生长速率。所有三个初始模型都很好地拟合了生长数据。改进的 Ratkowsky 二次模型很好地拟合了三个初始模型生成的生长速率（R 值范围为 0.96 至 0.98）。经过可接受的预测区 (APZ) 验证和拟合优度统计分析，确定 Baranyi 初始生长模型最适合这些数据。对于单速率指数冷却和两相线性冷却模型验证，所有 Baranyi 模型预测值（n 分别为 24 和 28）均在 APZ 内（-1.0 至 0.5 对数 CFU/g）。该模型将有助于机构食品服务场所确定经历较长冷却时间或在不当温度下储存的煮熟面食的安全性。实际应用：预测模型可用于估计冷却煮熟面食过程中的微生物生长程度，并用于设计 HACCP 计划和设定关键控制水平。零售食品行业将需要更少的挑战研究来验证其产品的安全性。该模型将为监管机构和食品行业提供一种客观的方法来评估微生物风险，并确保公众不会面临食物中毒的风险。
