U.S. Department of Agriculture, Agricultural Research Service, Residue Chemistry and Predictive Microbiology Research Unit, Room 2111, Center for Food Science and Technology, University of Maryland Eastern Shore, Princess Anne, Maryland 21853, USA.
J Food Prot. 2011 Feb;74(2):279-84. doi: 10.4315/0362-028X.JFP-10-314.
Salmonella spp. are a leading cause of foodborne illness. Mathematical models that predict Salmonella survival and growth on food from a low initial dose, in response to storage and handling conditions, are valuable tools for helping assess and manage this public health risk. The objective of this study was to develop and to validate the first predictive microbiology model for survival and growth of a low initial dose of Salmonella on chicken during refrigerated storage. Chicken skin was inoculated with a low initial dose (0.9 log) of a multiple antibiotic-resistant strain of Salmonella Typhimurium DT104 (ATCC 700408) and then stored at 4 to 12 °C for 0 to 10 days. A general regression neural network (GRNN) model that predicted log change of Salmonella Typhimurium DT104 as a function of time and temperature was developed. Percentage of residuals in an acceptable prediction zone, from -1 (fail-safe) to 0.5 (fail-dangerous) log, was used to validate the GRNN model by using a criterion of 70% acceptable predictions. Survival but not growth of Salmonella Typhimurium DT104 was observed at 4 to 8 °C. Maximum growth of Salmonella Typhimurium DT104 during 10 days of storage was 0.7 log at 9 °C, 1.1 log at 10 °C, 1.8 log at 11 °C, and 2.9 log at 12 °C. Performance of the GRNN model for predicting dependent data (n=163) was 85% acceptable predictions, for predicting independent data for interpolation (n=77) was 84% acceptable predictions, and for predicting independent data for extrapolation (n=70) to Salmonella Kentucky was 87% acceptable predictions. Thus, the GRNN model provided valid predictions for survival and growth of Salmonella on chicken during refrigerated storage, and therefore the model can be used with confidence to help assess and manage this public health risk.
肠炎沙门氏菌是食源性疾病的主要原因。能够预测食品中低初始剂量沙门氏菌的存活和生长的数学模型，是帮助评估和管理这种公共健康风险的有用工具。本研究的目的是开发和验证第一个预测微生物学模型，以预测冷藏储存期间鸡只上低初始剂量沙门氏菌的存活和生长。鸡皮接种了低初始剂量（0.9 对数）的多重抗生素耐药性肠炎沙门氏菌 Typhimurium DT104（ATCC 700408），然后在 4 至 12°C 下储存 0 至 10 天。开发了一个广义回归神经网络（GRNN）模型，该模型预测了沙门氏菌 Typhimurium DT104 的对数变化作为时间和温度的函数。通过使用 70%可接受预测的标准，将残差在可接受预测区间（-1（安全失效）至 0.5（危险失效）对数）内的百分比用于验证 GRNN 模型。在 4 至 8°C 下观察到肠炎沙门氏菌 Typhimurium DT104 的存活但没有生长。在 10 天的储存期内，沙门氏菌 Typhimurium DT104 的最大生长速度为 9°C 时为 0.7 对数，10°C 时为 1.1 对数，11°C 时为 1.8 对数，12°C 时为 2.9 对数。GRNN 模型对依赖数据（n=163）的预测性能为 85%可接受预测，对插值独立数据（n=77）的预测性能为 84%可接受预测，对 extrapolation 独立数据（n=70）到沙门氏菌 Kentucky 的预测性能为 87%可接受预测。因此，GRNN 模型为冷藏储存期间鸡只上沙门氏菌的存活和生长提供了有效的预测，因此该模型可以自信地用于帮助评估和管理这种公共健康风险。
