Cross‐contamination of ready‐to‐eat (RTE) salad vegetables with Salmonella from raw chicken followed by growth during meal preparation are important risk factors for human salmonellosis. To better predict and manage this risk, a model (general regression neural network) for growth of a chicken isolate of Salmonella Newport (0.91 log) on Romaine lettuce (0.18 g) at times (0–8 hr) and temperatures (16–40°C) observed during meal preparation was developed with Excel, NeuralTools, and @Risk. Model performance was evaluated using the acceptable prediction zones (APZ) method. The proportion of residuals in the APZ (pAPZ) was 0.93 for dependent data (n = 210) and 0.93 for independent data (n = 72) for interpolation. A pAPZ ≥0.70 indicates acceptable model performance. Thus, the model was successfully validated for interpolation and can be used with confidence to predict and manage this important risk to public health.

中文翻译：

---

长叶莴苣上新港沙门氏菌生长的预测模型

即食（RTE）色拉蔬菜与生鸡肉中的沙门氏菌交叉污染，随后在进餐过程中生长是人沙门氏菌病的重要危险因素。为了更好地预测和管理这种风险，建立了一个模型（通用回归神经网络），用于在（0–8小时）和温度（16–40小时）的长叶莴苣（0.18 g）上接种沙门氏菌新港鸡（0.91 log）用Excel，NeuralTools和@Risk开发了在进餐过程中观察到的温度（°C）。使用可接受的预测区域（APZ）方法评估模型性能。APZ中的残差比例（pAPZ）对于相关数据（n = 210）为0.93，对于独立数据（n = 210）为0.93= 72）进行插值。pAPZ≥0.70表示可接受的模型性能。因此，该模型已成功验证了内插法，可以放心地用于预测和管理这一对公共卫生的重要风险。