U.S. Department of Agriculture, Agricultural Research Service, USDA/1890 Center of Excellence in Poultry Food Safety Research, Room 2111, Center for Food Science and Technology, University of Maryland, Eastern Shore, Princess Anne, Maryland 21853, USA.
J Food Prot. 2009 Oct;72(10):2078-87. doi: 10.4315/0362-028x-72.10.2078.
A general regression neural network (GRNN) and Monte Carlo simulation model for predicting survival and growth of Salmonella on raw chicken skin as a function of serotype (Typhimurium, Kentucky, and Hadar), temperature (5 to 50 degrees C), and time (0 to 8 h) was developed. Poultry isolates of Salmonella with natural resistance to antibiotics were used to investigate and model survival and growth from a low initial dose (<1 log) on raw chicken skin. Computer spreadsheet and spreadsheet add-in programs were used to develop and simulate a GRNN model. Model performance was evaluated by determining the percentage of residuals in an acceptable prediction zone from -1 log (fail-safe) to 0.5 log (fail-dangerous). The GRNN model had an acceptable prediction rate of 92% for dependent data (n = 464) and 89% for independent data (n = 116), which exceeded the performance criterion for model validation of 70% acceptable predictions. Relative contributions of independent variables were 16.8% for serotype, 48.3% for temperature, and 34.9% for time. Differences among serotypes were observed, with Kentucky exhibiting less growth than Typhimurium and Hadar, which had similar growth levels. Temperature abuse scenarios were simulated to demonstrate how the model can be integrated with risk assessment, and the most common output distribution obtained was Pearson5. This study demonstrated that it is important to include serotype as an independent variable in predictive models for Salmonella. Had a cocktail of serotypes Typhimurium, Kentucky, and Hadar been used for model development, the GRNN model would have provided overly fail-safe predictions of Salmonella growth on raw chicken skin contaminated with serotype Kentucky. Thus, by developing the GRNN model with individual strains and then modeling growth as a function of serotype prevalence, more accurate predictions were obtained.
建立了一个通用回归神经网络（GRNN）和蒙特卡罗模拟模型，用于预测沙门氏菌在生鸡皮上的存活和生长，其功能与血清型（肠炎、肯塔基和哈达尔）、温度（5 至 50°C）和时间（0 至 8 小时）有关。使用具有天然抗生素抗性的禽源沙门氏菌分离株来研究和模拟从生鸡皮上的低初始剂量（<1 对数）开始的存活和生长。使用计算机电子表格和电子表格插件程序开发和模拟 GRNN 模型。通过确定残差在可接受预测范围内的百分比（-1 对数（失效安全）至 0.5 对数（失效危险））来评估模型性能。GRNN 模型对 464 个相关数据的预测准确率为 92%，对 116 个独立数据的预测准确率为 89%，超过了模型验证的 70%可接受预测率的性能标准。独立变量的相对贡献分别为血清型 16.8%、温度 48.3%和时间 34.9%。观察到血清型之间存在差异，肯塔基血清型的生长速度低于肠炎和哈达尔血清型，后两者的生长水平相似。模拟了温度滥用情况，以展示如何将该模型与风险评估相结合，最常见的输出分布是 Pearson5。本研究表明，在沙门氏菌预测模型中，将血清型作为独立变量非常重要。如果使用肠炎、肯塔基和哈达尔血清型混合物来开发模型，GRNN 模型将对受肯塔基血清型污染的生鸡皮上沙门氏菌的生长提供过度失效安全预测。因此，通过用单个菌株开发 GRNN 模型，然后根据血清型流行率来模拟生长，获得了更准确的预测。
