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Validation of a Salmonella Survival and Growth Model for Extrapolation to a Different Previous History: Frozen Storage Thomas P. Oscar, Agricultural Research.

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Presentation on theme: "Validation of a Salmonella Survival and Growth Model for Extrapolation to a Different Previous History: Frozen Storage Thomas P. Oscar, Agricultural Research."— Presentation transcript:

1 Validation of a Salmonella Survival and Growth Model for Extrapolation to a Different Previous History: Frozen 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; ; (fax); INTRODUCTION The USDA, ARS, Pathogen Modeling Program (PMP) is a computer software application that contains predictive models for cross-contamination, survival, growth, and death of foodborne pathogens. Validation of PMP models is an important step in their development because it increases confidence in using them to make food safety decisions. Some models in the PMP have been validated using the acceptable prediction zone (APZ) method 1,2. The APZ method systematically evaluates PMP models for goodness-of-fit, interpolation, and extrapolation using criteria for test data and model performance (Fig. 1). Only when a PMP model meets the criteria for goodness-of-fit and interpolation is it classified as validated. Evaluation of PMP models for extrapolation is done after validation and is important because it saves time and money by identifying conditions for which new PMP models are not needed. OBJECTIVE To evaluate a PMP model for survival and growth of Salmonella Typhimurium DT104 on chicken skin 3 for its ability to extrapolate to a new independent variable: previous frozen storage. MATERIALS AND METHODS Organism. A multiple antibiotic resistant strain (ATCC ) of Salmonella Typhimurium DT104 was used for model development and validation. Chicken preparation. Fresh chicken thighs were purchased at retail. Skin was removed, cut into circular portions and placed back on top of thigh meat. Chicken skin inoculation and incubation. Chicken skin portions were spot inoculated (5 ul) with S. Typhimurium DT104 (0.98 log/portion) followed by frozen storage for 6 days at -20°C. Chicken thighs were then thawed overnight at 4°C before incubation for 8 h at 5, 10, 15, 20, 25, 30, 35, 40, 45 or 50°C. Sampling and enumeration. At 0, 1, 2, 3, 4, 5, 6, 7, or 8 h of incubation, a chicken skin portion was placed into a small Whirl-Pak bag with 9 ml of BPW. The sample was then pulsified for 1 min to recover the pathogen into BPW. The concentration of S. 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. APZ method. To evaluate performance of the PMP model, residuals (observed – predicted values) for individual prediction cases were calculated. Next, the proportion of residuals in an APZ (pAPZ) from -1 log (fail-safe) to 0.5 log (fail-dangerous) was calculated and if pAPZ ≥ 0.682, then the PMP model was determined to provide acceptable predictions of the test data. RESULTS AND DISCUSSION Ability of the PMP model for survival and growth of Salmonella Typhimurium DT104 on chicken skin to extrapolate to a previous history of frozen storage was evaluated using the acceptable prediction zone (APZ) method (Fig. 1). The PMP model was validated for temperatures from 5 to 47.5°C but failed (pAPZ 47.5 to 50°C. Nonetheless, results of the APZ analysis (Table 1) indicated that the PMP model provided acceptable predictions (pAPZ ≥ 0.682) for all survival and growth curves obtained from 5 to 45°C following frozen storage at -20°C for 6 days. Thus, users of the PMP model can be confident that the model provides valid predictions of S. Typhimurium DT104 counts on fresh chicken as well as counts on chicken that has been frozen at -20°C for 6 days. More research is needed to see how broadly the PMP model can be applied to chicken that has been frozen. Additional data are needed to evaluate extrapolation of the PMP model to other times and temperatures of frozen storage as well as interactions of frozen storage with other independent variables such as inoculum size, strain variation and type of chicken meat. REFERENCES 1.Oscar, T. P J. Food Sci. 70:M129-M Oscar, T. P J. Food Prot. 74: Oscar, T. P J. Food Prot. 72: ACKNOWLEDGMENT The author would like to thank Jacquelyn Ludwig of ARS (retired) for her assistance on this project. Were data used in model development? Step 1: Goodness-of-fit NOFail Was pAPZ ≥ 0.682? YES NOFail YES Acceptable goodness-of-fit Did model pass evaluation for goodness-of-fit? Step 2: Interpolation NOFail Were data used in model development? YES Fail NO Were data collected with same methods as data used in model development? NOFail YES Did data provide uniform coverage of the matrix of independent variables? NOFail YES Was pAPZ ≥ 0.682?NOFail YES Validated model Did model pass evaluation for interpolation? Step 3: Extrapolation NOFail Were data used in model development? YES Fail NO Were data collected with same methods as data used in model development except for independent variable being evaluated? NOFail YES Did data provide uniform coverage of the matrix of independent variables? NOFail YES Was pAPZ ≥ 0.682?NOFail YES Validated for extrapolation to a new independent variable Fig. 2. Survival and growth curves for Salmonella Typhimurium DT104 on chicken skin following frozen storage at -20°C for 6 days. Most counts were below those predicted by the PMP model indicating that frozen storage had injured some of the Salmonella. Table 1. Performance of the PMP model for extrapolation to a new independent variable: previous frozen storage at -20°C for 6 days EvaluationType of dataTemperature (°C)AcceptableTotalpAPZ a Goodness-of-fitDependent to InterpolationIndependent to ExtrapolationIndependent to a Proportion of residuals in an acceptable prediction zone (pAPZ) from -1 log (fail-safe) to 0.5 log (fail-dangerous). Fig. 1. Acceptable prediction zone (APZ) method for evaluating performance and validating PMP models.


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