Submitted to: Journal of Food Science and Technology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/21/2017
Publication Date: 8/11/2017
Citation: Oscar, T.P. 2017. Modeling the effect of inoculum size on the thermal inactivation of Salmonella Typhimurium to elimination in ground chicken thigh meat. Journal of Food Science and Technology. 5(4):135-142.
Interpretive Summary: The death of Salmonella in chicken during cooking is affected by many factors, such as time, temperature, type of Salmonella, and the composition, size, and shape of the chicken meat product. In the present study, we investigated and modeled the effect of different initial numbers of Salmonella on their rate of death during cooking of ground chicken. We observed that the time needed to eliminate the Salmonella from the ground chicken decreased as the cooking temperature increased from 126F to 212F. In addition, we found that the time to eliminate Salmonella increased as the initial number of the pathogen increased. A model was successfully developed that can predict the death of Salmonella over time and as a function of cooking temperature and the initial number of Salmonella in the ground chicken. This new model will be a valuable tool that can be used to predict when different levels of Salmonella contamination have been eliminated from ground chicken during cooking at different temperatures.
Technical Abstract: A study was undertaken to investigate and model the effect of inoculum size on the thermal inactivation of Salmonella to elimination in ground chicken by conduction heating. To develop the model, ground chicken thigh meat portions (0.76 cm3) in microcentrifuge tubes were inoculated with 2.0, 3.6, or 5.2 log of a single strain of Salmonella Typhimurium followed by cooking for 0 to 10 min at 52 to 100 degrees C in a heating block. To validate the model, the ground chicken portions were inoculated with 2.8 or 4.4 log of S. Typhimurium followed by cooking for 0 to 9 min at 55 to 97 degrees C. An automated, whole sample enrichment, miniature most probable number (MPN) method with a lower limit of detection of one Salmonella cell per portion was used for enumeration. The MPN data were used to develop (n = 851) and validate (n = 256) a multiple layer feedforward neural network model with two hidden layers of two nodes each. Model performance was evaluated using the acceptable prediction zone (APZ) method. The proportion of residuals in an APZ (pAPZ) from -1 log (fail-safe) to 0.5 log (fail-dangerous) was 0.945 (804/851) for dependent data and 0.945 (242/256) for independent data for interpolation. A pAPZ = 0.7 indicated that model predictions had acceptable bias and accuracy. Thus, the model was successfully validated. The time for elimination of Salmonella at 58 degrees C was 5.6, 7.1, and 8.7 min for inoculum sizes of 2.0, 3.6 and 5.2 log per portion, respectively. This relationship was observed for all cooking temperatures and among all inoculum sizes investigated indicating that inoculum size was an important independent variable to include in the model.