Development and Validation of a Predictive Microbiology Model for Survival and Growth of Salmonella on Chicken Stored at 4 to 12°C

Abstract

Salmonella spp. are a leading cause of foodborne illness. Mathematical models that predict Salmonella survival and growth on food from a low initial dose, in response to storage and handling conditions, are valuable tools for helping assess and manage this public health risk. The objective of this study was to develop and to validate the first predictive microbiology model for survival and growth of a low initial dose of Salmonella on chicken during refrigerated storage. Chicken skin was inoculated with a low initial dose (0.9 log) of a multiple antibiotic-resistant strain of Salmonella Typhimurium DT104 (ATCC 700408) and then stored at 4 to 12 °C for 0 to 10 days. A general regression neural network (GRNN) model that predicted log change of Salmonella Typhimurium DT104 as a function of time and temperature was developed. Percentage of residuals in an acceptable prediction zone, from -1 (fail-safe) to 0.5 (fail-dangerous) log, was used to validate the GRNN model by using a criterion of 70% acceptable predictions. Survival but not growth of Salmonella Typhimurium DT104 was observed at 4 to 8 °C. Maximum growth of Salmonella Typhimurium DT104 during 10 days of storage was 0.7 log at 9 °C, 1.1 log at 10 °C, 1.8 log at 11 °C, and 2.9 log at 12 °C. Performance of the GRNN model for predicting dependent data (n=163) was 85% acceptable predictions, for predicting independent data for interpolation (n=77) was 84% acceptable predictions, and for predicting independent data for extrapolation (n=70) to Salmonella Kentucky was 87% acceptable predictions. Thus, the GRNN model provided valid predictions for survival and growth of Salmonella on chicken during refrigerated storage, and therefore the model can be used with confidence to help assess and manage this public health risk.

Development and validation of a neural network model for predicting growth of Salmonella Newport on diced Roma tomatoes during simulated salad preparation and serving: extrapolation to other serotypes

“…At each sampling time, four samples were removed, one per test temperature, and then 0.7 mL of BPW at 4 °C was added to stop the growth of Salmonella on the tomato portion. Salmonella survive and do not grow or die for 10 days of storage at 4 °C on ground chicken with native microflora (Oscar, ). Thus, 4 °C is a good temperature for stopping the growth of Salmonella for accurate enumeration of this pathogen on or in a food portion at the time of sampling.…”

General Regression Neural Network Model for Behavior of Salmonella on Chicken Meat during Cold Storage

“…In the current study, the number of Salmonella Typhimurium DT104 on chicken meat declined slightly (0.4 log) during storage for 8 d at 4°C. Other studies (Szczawinska and others 1991;Nychas and Tassou 1996;Oscar 2011a;Pradhan and others 2012) report that the level of Salmonella on chicken meat stays the same during refrigerated storage. Together these studies indicate that behavior of Salmonella on chicken meat during refrigerated storage is complex and may depend on multiple factors whose interactions are not well understood at the present time.…”

“…Zaher and Fujikawa () investigated behavior of Salmonella Enteritidis in ground chicken stored at 8 to 16 °C and Pradhan and others () investigated behavior of Salmonella Typhimurium on chicken white meat stored for 21 d at 0, 4, or 8 °C. Oscar () studied survival and growth of Salmonella Typhimurium DT104 on chicken skin stored for 0 to 10 d at 4 to 12 °C. However, none of these studies investigated the effect of all types of chicken meat on behavior of Salmonella during refrigerated storage.…”

A review on growth/no growth Salmonella models

“…The risk pathways map is modeled as a series of unit operations and associated pathogens in which probability distributions for rare events such as the probability of growth may be included. Oscar (2011) also considered as rare event the possibility of finding a pathogen in a food, modeled with probability distributions coupled with random sampling methods. -Food matrices and microbial interactions are factors that can influence the probability for Salmonella cells to grow.…”