American Journal of Food Science and Technology
ISSN (Print): 2333-4827 ISSN (Online): 2333-4835 Website: Editor-in-chief: Hyo Choi
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American Journal of Food Science and Technology. 2017, 5(4), 135-142
DOI: 10.12691/ajfst-5-4-3
Open AccessArticle

Modeling the Effect of Inoculum Size on the Thermal Inactivation of Salmonella Typhimurium to Elimination in Ground Chicken Thigh Meat

Thomas P. Oscar1,

1Department of Agriculture, Agricultural Research Service, Residue Chemistry and Predictive Microbiology Research Unit, Center for Food Science and Technology, University of Maryland Eastern Shore, Princess Anne, Maryland, U.S.

Pub. Date: August 01, 2017

Cite this paper:
Thomas P. Oscar. Modeling the Effect of Inoculum Size on the Thermal Inactivation of Salmonella Typhimurium to Elimination in Ground Chicken Thigh Meat. American Journal of Food Science and Technology. 2017; 5(4):135-142. doi: 10.12691/ajfst-5-4-3


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°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°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°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.

ground chicken Salmonella Typhimurium thermal inactivation neural network modeling inoculum size validation

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