Predictive model for growth of Salmonella Newport on Romaine lettuce

Abstract

AbstractCross‐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.