Predictive Model for Survival and Growth of Salmonella on Chicken during Cold Storage

1. Predictive Model for Survival and Growth of Salmonella on Chicken during Cold Storage Thomas P. Oscar, Agricultural Research Service, USDA, Room 2111, Center for Food Science and Technology, University of Maryland Eastern Shore, Princess Anne, MD 21853; 410-651-6062; 410-651-8498 (fax); thomas.oscar@ars.usda.gov INTRODUCTION Predictive models are valuable tools for assessing and managing the risk of illness from food contaminated with human bacterial pathogens like Salmonella. During cold storage of chicken, the number of Salmonella on chicken may stay the same, increase, or decrease depending on the time and temperature of storage and the type of chicken meat (i.e. white, dark, or skin). For example, Foster and Mead1 found that survival of Salmonella Typhimurium during frozen storage was better in breast meat than in thigh meat and was better at -20°C than at -5°C or -2°C. They did not investigate survival of Salmonella Typhimurium on chicken skin nor did they collect enough data to develop a predictive model. Consequently, the current study was undertaken to investigate and model the behavior of Salmonella on all three types of chicken meat during frozen and refrigerated storage. MATERIALS AND METHODS Organism. A multiple antibiotic resistant strain (ATCC 700408) of Salmonella Typhimurium DT104 was used for model development and validation. Chicken preparation.Ground breast and thigh meat were packed into separate plastic petri dishes (100 x 15 mm), frozen at -20°C, and then cut with a cork borer into cylindrical portions (0.75 cm3), which were transferred to 1.5 ml micro-centrifuge tubes (Figure 1). Circular skin portions (0.5 cm2) were placed on top of some of the breast or thigh meat portions. Chicken skin inoculation and incubation. Chicken portions were spot inoculated (5 μl) with SalmonellaTyphimurium DT104 (2.8 log) and then incubated in a heating/cooling block (Figure 2) at -8, -4, 0, 4, 8, 10, 12, 14, or 16°C. Sampling and enumeration. At 0, 1, 2, 4, 6, and 8 days of incubation, a chicken meat portion was placed into a small Whirl-Pak bag with 9 ml of buffered peptone water (BPW). The sample was pulsified for 1 min to recover the pathogen into BPW. The concentration of Salmonella Typhimurium DT104 in the BPW was determined by most probable number and spiral plating methods and was used to calculate the number of pathogen cells on the skin portion. Model development. A dataset (n = 717) was created in an Excel spreadsheet with separate columns for the independent variables (type of chicken meat, temperature, and time) and the dependent variable (log/portion). A general regression neural network (GRNN) model was developed using NeuralTools (Figure 3) with 80% of the data used to train the GRNN and 20% of the data used to test the GRNN for its ability to generalize. Model performance. Residuals (observed – predicted) were considered acceptable when then fell in an acceptable prediction zone from -1 log (fail-safe) to 0.5 log (fail-dangerous). Performance of the model for the test data was considered acceptable when the proportion of residuals in the acceptable prediction zone (pAPZ) was ≥ 0.7. Figure 5 Figure 3 Statistical analysis. Within a storage temperature, effects of storage time, type of chicken meat, and their interaction on the dependent variable (log/portion) were assessed by two-way analysis of variance. When a significant main effect or interaction was observed, the mean for breast meat (B) or skin (S) was compared to the mean for thigh meat (T) using a Bonferonni post-test with a significance level of P < 0.05. RESULTS AND DISCUSSION Although there were two minor differences (Figure 4A and 4D) in log counts among types of chicken meat, one at -8°C and one at 4°C, in general, Salmonella Typhimurium DT104 numbers on chicken stayed the same during 8 days of storage at -8 to 10°C (Figure 4A-F). In contrast, at 12, 14, and 16°C (Figure 4G-I), log counts of Salmonella Typhimurium DT104 on chicken increased (P < 0.05) as a function of storage time and temperature. In addition, growth of Salmonella Typhimurium DT104 was highest on thigh meat, intermediate on skin, and lowest on breast meat. • An outcome of the training of the GRNN model was an assessment of the relative impact of the independent variables on the dependent variable, which were: 1) temperature (54.8%); 2) time (30.6%); and 3) type of chicken meat (14.6%). Performance of the GRNN model was evaluated by assessing its ability to predict individual combinations of the independent variables using the acceptable prediction zone (APZ) method2,3 (Figure 5). • There were only three combinations of independent variables that had pAPZ values that were unacceptable (i.e. < 0.7). These were: 1) 12°C, 8 days of storage, and skin, which had a pAPZ of 0.57 (4/7; Fig. 5G); 2) 14°C, 6 days of storage, and skin, which had a pAPZ of 0.67 (4/6; Fig. 5H); and 3) 14°C, 8 days of storage, and skin, which had a pAPZ of 0.5 (3/6; Fig. 5H). The mean residuals for these three conditions were all within the APZ and were: 1) 0.15 log; 2) 0.14 log; and 3) -0.02 log, respectively. Thus, these local prediction problems were not the result of prediction bias by the model but rather were due to variation in the data that resulted in unacceptable prediction accuracy by the model, which could indicate that an important independent variable is missing from the model for these conditions. • SIGNIFICANCE • Results of this study indicate that it is important to include type of chicken meat as an independent variable in the GRNN model and that the GRNN model can be used with confidence to assess and manage effects of cold storage deviations on the risk of salmonellosis from chicken with the exception of extended storage times on skin stored at 12 or 14°C. • REFERENCES • Foster, R.D. and Mead, G.C. 1976. J. Appl. Bacteriol. 41:505-510. • Oscar, T. P. 2005. J. Food Sci. 70:M129-M137. • Oscar, T. P. 2005. J. Food Prot. 68:2606-2613. • ACKNOWLEDGMENT • The author would like to thank Margo Wright of USDA, ARS and Moira Imegwu of UMES for their technical assistance on this project. Figure 2 Figure 1 Figure 4