Neural Network Model for Thermal Inactivation of Salmonella Typhimurium to Elimination in Ground Chicken: Acquisition of Data by Whole Sample Enrichment, Miniature Most-Probable-Number Method

摘要

Predictive models are valuable tools for assessing food safety. Existing thermal inactivation models for Salmonella and ground chicken do not provide predictions above 71℃, which is below the recommended final cooked temperature of 73.9℃ for chicken. They also do not predict when all Salmonella are eliminated without extrapolating beyond the data used to develop them. Thus, a study was undertaken to develop a model for thermal inactivation of Salmonella to elimination in ground chicken at temperatures above those of existing models. Ground chicken thigh portions (0.76 cm~3) in microcentrifuge tubes were inoculated with 4.45 ± 0.25 log most probable number (MPN) of a single strain of Salmonella Typhimurium (chicken isolate). They were cooked at 50 to 100℃ in 2 or 2.5℃ increments in a heating block that simulated two-sided pan frying. A whole sample enrichment, miniature MPN (WSE-mMPN) method was used for enumeration. The lower limit of detection was one Salmonella cell per portion. MPN data were used to develop a multiple-layer feedforward neural network model. Model performance was evaluated using the acceptable prediction zone (APZ) method. The proportion of residuals in an APZ (pAPZ) from -1 log (failsafe) to 0.5 log (fail-dangerous) was 0.911 (379 of 416) for dependent data and 0.910 (162 of 178) for independent data for interpolation. A pAPZ >0.7 indicated that model predictions had acceptable bias and accuracy. There were no local prediction problems because pAPZ for individual thermal inactivation curves ranged from 0.813 to 1.000. Independent data for interpolation satisfied the test data criteria of the APZ method. Thus, the model was successfully validated. Predicted times for a 1-log reduction'ranged from 9.6 min at 56°C to 0.71 min at 100℃. Predicted times for elimination ranged from 8.6 min at 60℃ to 1.4 min at 100℃. The model will be a valuable new tool for predicting and managing this important risk to public health.