Development and validation of a neural network model for growth of Salmonella Newport from chicken on cucumber for use in risk assessment

Abstract

A neural network model was developed for predicting growth of a chicken isolate of Salmonella Newport on cucumber portions as a function of times (0 to 8 hr) and temperatures (16 to 40℃) observed during meal preparation and serving for use in risk assessment. Model development and validation were accomplished using the test data, model performance, and model validation criteria of the Acceptable Prediction Zones (APZ) method in the Validation Software Tool (ValT). The model was considered to provide acceptable predictions when the proportion of residuals in the APZ (pAPZ) was ≥0.70. Data for model development (n = 140) and validation (n = 72) satisfied all criteria of the APZ method in ValT with pAPZ of 0.97 and 0.93, respectively. Thus, the model was successfully validated and can be used with confidence in risk assessment to predict growth of Salmonella Newport from chicken on cucumber during meal preparation and serving. Novelty impact statement The model can be used by risk assessors to help predict variability and uncertainty of consumer exposure to Salmonella from individual lots of chicken produced by a farm-to-table scenario that includes cross-contamination of sliced cucumbers with Salmonella Newport from raw chicken followed by growth of Salmonella Newport on cucumber for times and temperatures observed during meal preparation and serving.