Poultry Food Assess Risk Model for Salmonella and Chicken Gizzards: III. Dose Consumed Step.

The Dose Consumed step of the Poultry Food Assess Risk Model (PFARM) for Salmonella and chicken gizzards was presented and compared to the Exposure Assessment step of Quantitative Microbial Risk Assessment (QMRA). The specific objectives were 1) to demonstrate the dose consumed step of PFARM for Salmonella and chicken gizzards; 2) to compare Salmonella dose consumed from cooked chicken gizzards to that from cross-contaminated and temperature-abused lettuce; 3) to determine if Salmonella dose consumed changed over time in a production chain; and 4) to compare PFARM and QMRA predictions of Salmonella dose consumed. The PFARM and QMRA were developed in an Excel notebook and simulated with @Risk. Salmonella prevalence and number data (P = 100) for chicken gizzards (56 g) and scenario analysis were used to address objectives 1, 2, and 4, whereas running windows of 60 consecutive chicken gizzard samples and scenario analysis were used to address objective 3. A lot size of 1,000 kg of chicken gizzards was simulated. Mean portion size was 168 g resulting in the simulation of 5,952 meals per lot. Of these, 3.69 ± 0.32% and 0.49 ± 0.07% (mean ± SD) resulted in Salmonella dose consumed of ≥1 per meal from cooked chicken gizzards and lettuce, respectively. However, the total Salmonella dose consumed per lot from cooked chicken gizzards (272 ± 27) was less (P ≤ 0.05) than from lettuce (6,050 ± 4,929) because of a few highly contaminated (>310 Salmonella) lettuce portions at consumption. Over time in the production chain, Salmonella prevalence and total dose consumed per lot changed (P ≤ 0.05) but the patterns differed. The QMRA predicted higher (P ≤ 0.05) Salmonella dose consumed per meal than PFARM. In part, this was because QMRA only simulated contaminated grams, whereas PFARM simulated contaminated and non-contaminated meals. However, other factors, which are discussed, also contributed to the overestimation of Salmonella dose consumed by QMRA.

Poultry Food Assess Risk Model for Salmonella and Chicken Gizzards: II. Illness Dose Step.

The Illness Dose (ID) step of a Poultry Food Assess Risk Model (PFARM) for Salmonella and chicken gizzards (CGs) was shown in the present study. The illness dose is the minimum dose of Salmonella consumed that causes an illness. It depends on the zoonotic potential (ZP) of Salmonella, food consumption behavior (FCB), and consumer health and immunity (CHI) or the disease triangle (DT). Zoonotic potential is the ability of Salmonella to survive, grow, and spread in the production chain or food and then cause illness in humans. Illness dose is predicted in PFARM using a DT, dose-response model (DRM) that was developed with human feeding trial (HFT) data and was validated with human outbreak investigation (HOI) data for Salmonella. The ability of the DT, DRM to predict DR data from HOI and HFT for Salmonella was quantified using the Acceptable Prediction Zone (APZ) method where acceptable performance occurred when the proportion of residuals in the APZ (pAPZ) was ≥0.7. United States, Centers for Disease Control and Prevention (CDC) data for human salmonellosis from 2007 to 2016 were used to simulate ZP, and only minor changes in ZP of 11 Salmonella serotypes were observed during this time. The performance of the DT, DRM for predicting Salmonella DR data from HFT and HOI was acceptable with pAPZ that ranged from 0.87 to 1 for individual serotypes of Salmonella. Simulation results from the DT, DRM in PFARM indicated that ID decreased (P ≤ 0.05) and ZP increased (P ≤ 0.05) over time in the simulated production chain because the main serotype of Salmonella changed from Kentucky (low ZP) to Infantis (high ZP) while FCB and CHI were held constant. These results indicated that the DT, DRM in PFARM can be used with confidence to predict ID as a function of ZP, FCB, and CHI. In other words, the DT, DRM in PFARM can be used with confidence to predict dose-response for Salmonella and CGs.

Poultry Food Assess Risk Model for Salmonella and Chicken Gizzards: I. Initial Contamination.

The Poultry Food Assess Risk Model (PFARM) project was initiated in 1995 to develop data collection and modeling methods for simulating the risk of salmonellosis from poultry food produced by individual production chains. In the present study, the Initial Contamination (IC) step of PFARM for Salmonella and chicken gizzards (CG) was conducted as a case study. Salmonella prevalence (Pr), number (N), and serotype/zoonotic potential (ZP) data (n = 100) for one sample size (56 g) of CG were collected at meal preparation (MP), and then Monte Carlo simulation (MCS) was used to obtain data for other sample sizes (112, 168, 224, 280 g). The PFARM was developed in Excel and was simulated with @Risk. Data were simulated using a moving window of 60 samples to determine how Salmonella Pr, N, and ZP changed over time in the production chain. The ability of Salmonella to survive, grow, and spread in the production chain and food, and then cause disease in humans was ZP, which was based on U. S. Centers for Disease Control and Prevention data for salmonellosis. Of 100 CG samples tested, 35 were contaminated with Salmonella with N from 0 to 0.809 (median) to 2.788 log per 56 g. Salmonella serotype Pr per 56 g was 16% for Kentucky (ZPmode = 1.1), 9% for Infantis (ZPmode = 4.4), 6% for Enteritidis (ZPmode = 5.0), 3% for Typhimurium (ZPmode = 4.9), and 1% for Thompson (ZPmode = 3.7). Results from MCS indicated that Salmonella Pr, N, and ZP among portions of CG at MP changed (P ≤ 0.05) over time in the production chain. Notably, the main serotype changed from Kentucky (low ZP) to Infantis (high ZP). However, the pattern of change for Salmonella Pr, N, and ZP differed over time in the production chain and by the statistic used to characterize it. Thus, a performance standard (PS) based on Salmonella Pr, N, or ZP at testing or MP will likely not be a good indicator of poultry food safety or risk of salmonellosis.

Monte Carlo Simulation Model for Predicting Salmonella Contamination of Chicken Liver as a Function of Serving Size for Use in Quantitative Microbial Risk Assessment.

ABSTRACT: The first step in quantitative microbial risk assessment (QMRA) is to determine the distribution of pathogen contamination among servings of the food in question at some point in the farm-to-table chain. In the present study, the distribution of Salmonella contamination among servings of chicken liver for use in the QMRA was determined at meal preparation. Salmonella prevalence (P), most probable number (MPN, N), and serotype for different serving sizes were determined by use of a combination of five methods: (i) whole sample enrichment; (ii) quantitative PCR; (iii) culture isolation; (iv) serotyping; and (v) Monte Carlo simulation. Epidemiological data also were used to convert serotype data to virulence (V) values for use in the QMRA. A Monte Carlo simulation model based in Excel and simulated with @Risk predicted Salmonella P, N, serotype, and V as a function of a serving size of one (58 g) to eight (464 g) chicken livers. Salmonella P of chicken livers was 72.5% (58 of 80) per 58 g. Four Salmonella serotypes were isolated from chicken livers: (i) Infantis (P = 28%, V = 4.5); (ii) Enteritidis (P = 15%, V = 5); (iii) Typhimurium (P = 15%, V = 4.8); and (iv) Kentucky (P = 15%, V = 0.8). Salmonella N was 1.76 log MPN/58 g (median) with a range of 0 to 4.67 log MPN/58 g, and the median Salmonella N was not affected (P > 0.05) by serotype. The model predicted a nonlinear increase (P ≤ 0.05) of Salmonella P from 72.5%/58 g to 100%/464 g, a minimum N of 0 log MPN/58 g to 1.28 log MPN/464 g, and a median N from 1.76 log MPN/58 g to 3.22 log MPN/464 g. Regardless of serving size, predicted maximum N was 4.74 log MPN per serving, mean V was 3.9 per serving, and total N was 6.65 log MPN per lot (10,000 chicken livers). The data acquired and modeled in this study address an important data gap in the QMRA for Salmonella and whole chicken liver.

A multiple therapy hypothesis for treatment of COVID-19 patients.

The coronavirus disease 2019 (COVID-19) pandemic, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has killed more than one million people as of October 1, 2020. Consequently, a search is on for a treatment that can bring the pandemic to an end. However, treatments (vaccine, antiviral, plasma) that are directed at specific viral proteins (RNA polymerase, spike proteins) may not work well against all strains of the virus. Therefore, it is hypothesized that a therapy based on multiple treatments is needed for COVID-19 patients and to bring the pandemic to an end. Here, it is proposed that a combination of cool air therapy (CAT) and purified air technology (PAT) in an oxygen species cool air respirator (OSCAR) could be used to reduce viral (SARS-CoV-2) load and severity of illness in COVID-19 patients through the individual dose-response relationship. In addition, the proposed therapy (CAT + PAT in OSCAR), which works by a more general physical and chemical mechanism, should work well with other treatments (vaccine, antiviral, plasma) that target specific viral proteins (RNA polymerase, spike proteins) to provide a safe and effective multiple therapy approach for ending the COVID-19 pandemic caused by SARS-CoV-2.

Short-term and long-term effects of pathogen reduction interventions on salmonellosis from whole chickens.

The current study was undertaken to evaluate short-term and long-term effects of pathogen reduction interventions on food safety. This was accomplished using a model that predicts risk of salmonellosis from whole chickens produced by different scenarios. Interventions investigated were a 50% pathogen reduction before retail (PR), a 50% pathogen reduction at serving by consumer education (CE), and a 75% pathogen reduction by PR + CE. Long-term effects were simulated by reducing consumer resistance by an amount equal to reductions in pathogen exposure caused by interventions in the short-term. In the short-term, risk of salmonellosis was reduced (p < 0.05) from 0.42 to 0.21, 0.23, and 0.13 cases per 100,000 consumers by PR, CE, and PR + CE, respectively. However, in the long-term, risk of salmonellosis was increased (p < 0.05) from 0.42 to 1.03, 1.08, and 2.20 cases per 100,000 consumers by PR, CE, and PR + CE, respectively. These results indicated that food safety benefits of pathogen reduction interventions reversed with time because of a decrease in consumer resistance to salmonellosis.

General regression neural network model for behavior of Salmonella on chicken meat during cold storage.

UNLABELLED: A study was undertaken to investigate and model behavior of Salmonella on chicken meat during cold storage at constant temperatures. Chicken meat (white, dark, or skin) portions (0.75 cm(3)) were inoculated with a single strain of Salmonella Typhimurium DT104 (2.8 log) followed by storage for 0 to 8 d at -8, 0, 8, 12, 14, or 16 °C for model development and at -4, 4, 10, or 14 °C for model validation. A general regression neural network model was developed with commercial software. Performance of the model was considered acceptable when the proportion of residuals (observed--predicted) in an acceptable prediction zone (pAPZ) from -1 log (fail-safe) to 0.5 logs (fail-dangerous) was ≥ 0.7. Growth of Salmonella Typhimurium DT104 on chicken meat was observed at 12, 14, and 16 °C and was highest on dark meat, intermediate on skin, and lowest on white meat. At lower temperatures (-8 to 10 °C) Salmonella Typhimurium DT104 remained at initial levels throughout 8 d of storage except at 4 °C where there was a small (0.4 log) but significant decline. The model had acceptable performance (pAPZ = 0.929) for dependent data (n = 482) and acceptable performance (pAPZ = 0.923) for independent data (n = 235). Results indicated that it is important to include type of meat as an independent variable in the model and that the model provided valid predictions of the behavior of Salmonella Typhimurium DT104 on chicken skin, white, and dark meat during storage for 0 to 8 d at constant temperatures from -8 to 16 °C. PRACTICAL APPLICATION: A model for predicting behavior of Salmonella on chicken meat during cold storage was developed and validated. The model will help the chicken industry to better predict and manage this risk to public health.

Growth of Salmonella Typhimurium DT104 at 30°C is not affected by anatomical location on the chicken carcass.

Development of models for growth of Salmonella in the chicken food matrix is time-consuming and expensive. The current study was undertaken to examine growth of Salmonella on different anatomical locations of the chicken carcass. The purpose was to determine whether anatomical location should be included as an independent variable in predictive models for chicken. Eleven anatomical locations were studied: skin (wing, breast, drumstick, and thigh), meat surface (wing, breast, drumstick, and thigh), and meat interior (breast, drumstick, and thigh). Background microflora, pH, and growth (lag time, λ; growth rate, µ; and time for a 3-log increase, t(3)) at 30°C for a small inoculum size (0.92 ± 0.30 log per portion) of Salmonella Typhimurium DT104 were examined. Four or six replicate storage trials were conducted per anatomical location (n = 46 growth curves). Portion sizes were 1.12 ± 0.17 g (mean ± standard deviation) for meat and 0.25 ± 0.08 g for skin. A two-phase linear model was used to determine λ and µ. The effect of anatomical location on dependent variables was assessed by one-way analysis of variance. pH values differed (P < 0.001) among anatomical locations, with skin (6.86 ± 0.20). dark meat (6.39 ± 0.20) . white meat (5.97 ± 0.20). Background microflora (4.32 ± 1.66 log per portion) was variable and not affected (P > 0.05) by anatomical location. Likewise, λ (1.90 ± 0.75 h), µ (0.648 ± 0.120 log/h), and t(3) (6.71 ± 0.82 h) at 30°C were not affected (P > 0.05) by anatomical location. Although there were differences in pH among anatomical locations, these differences were not sufficient to affect growth of Salmonella Typhimurium DT104 at 30°C. If this observation holds for other storage conditions and strains, then anatomical location does not need to be included as an independent variable in predictive models for chicken. This would save significant time and money for the predictive microbiologist.

Extrapolation of a predictive model for growth of a low inoculum size of Salmonella Typhimurium DT104 on chicken skin to higher inoculum sizes.

Validation of model predictions for independent variables not included during model development can save time and money by identifying conditions for which new models are not needed. A single strain of Salmonella Typhimurium DT104 was used to develop a general regression neural network (GRNN) model for growth of a low inoculum size (0.9 log) on chicken skin with native microflora as a function of time (0 to 8 h) and temperature (20 to 45°C). The ability of the GRNN model to predict growth of higher inoculum sizes (2, 3, or 4.1 log) was evaluated. When the proportion of residuals in an acceptable prediction zone (pAPZ) from -1 log (fail-safe) to 0.5 log (fail-dangerous) was ≥0.7, the GRNN model was classified as providing acceptable predictions of the test data. The pAPZ for dependent data was 0.93 and for independent data for interpolation was 0.88. The pAPZs for extrapolation to higher inoculum sizes of 2, 3, or 4.1 log were 0.92, 0.73, and 0.77, respectively. However, residual plots indicated local prediction problems with pAPZs of < 0.7 for an inoculum size of 3 log at 30, 35, and 40°C and for an inoculum size of 4.1 log at 35 and 40°C where predictions were fail-dangerous, indicating faster growth at higher inoculum sizes. The model provided valid predictions of Salmonella Typhimurium DT104 growth on chicken skin from inoculum sizes of 0.9 and 2 log at all temperatures investigated and from inoculum sizes of 3 and 4.1 log at some but not all temperatures investigated. Thus, the model can be improved by including inoculum size as an independent variable.

Development and validation of a predictive microbiology model for survival and growth of Salmonella on chicken stored at 4 to 12 °C.

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.

Survival of Campylobacter jejuni and Campylobacter coli on retail broiler meat stored at -20, 4, or 12 degrees C and development of Weibull models for survival.

Survival of Campylobacter jejuni and Campylobacter coli isolated from broiler meat was investigated and modeled on retail breast meat. Meat portions were inoculated with C. jejuni or C. coli at 6.4 to 6.8 log CFU/g followed by storage at -20 degrees C for 84 days or at 4 or 12 degrees C for 14 days. Kinetic data within a species and temperature were fitted to the Weibull model. When >or=70% of the residuals were in an acceptable prediction zone from -1 (fail-safe) to 0.5 (fail-dangerous) log units, the model was considered to have acceptable performance. Survival of Campylobacter was highest at 4 degrees C, lowest at 12 degrees C, and intermediate at -20 degrees C. Survival of C. jejuni and C. coli was similar at -20 degrees C but was lower (P<0.05) for C. jejuni than for C. coli at 4 and 12 degrees C. The Weibull model provided acceptable predictions for four of six sets of dependent data with unacceptable performance for survival of C. jejuni at -20 and 12 degrees C. A difference in survival was observed between the two strains of C. jejuni tested. Comparison of Weibull model predictions with data for C. jejuni archived in ComBase revealed mostly unacceptable performance, indicating that C. jejuni and C. coli survival on raw broiler breast meat differs from published results for other strains and growth media. Variation in Campylobacter survival among replicate storage trials was high, indicating that performance of the models can be improved by collection of additional data to better define the survival response during storage at temperatures from -20 to 12 degrees C.

General regression neural network and monte carlo simulation model for survival and growth of salmonella on raw chicken skin as a function of serotype, temperature, and time for use in risk assessment.

A general regression neural network (GRNN) and Monte Carlo simulation model for predicting survival and growth of Salmonella on raw chicken skin as a function of serotype (Typhimurium, Kentucky, and Hadar), temperature (5 to 50 degrees C), and time (0 to 8 h) was developed. Poultry isolates of Salmonella with natural resistance to antibiotics were used to investigate and model survival and growth from a low initial dose (<1 log) on raw chicken skin. Computer spreadsheet and spreadsheet add-in programs were used to develop and simulate a GRNN model. Model performance was evaluated by determining the percentage of residuals in an acceptable prediction zone from -1 log (fail-safe) to 0.5 log (fail-dangerous). The GRNN model had an acceptable prediction rate of 92% for dependent data (n = 464) and 89% for independent data (n = 116), which exceeded the performance criterion for model validation of 70% acceptable predictions. Relative contributions of independent variables were 16.8% for serotype, 48.3% for temperature, and 34.9% for time. Differences among serotypes were observed, with Kentucky exhibiting less growth than Typhimurium and Hadar, which had similar growth levels. Temperature abuse scenarios were simulated to demonstrate how the model can be integrated with risk assessment, and the most common output distribution obtained was Pearson5. This study demonstrated that it is important to include serotype as an independent variable in predictive models for Salmonella. Had a cocktail of serotypes Typhimurium, Kentucky, and Hadar been used for model development, the GRNN model would have provided overly fail-safe predictions of Salmonella growth on raw chicken skin contaminated with serotype Kentucky. Thus, by developing the GRNN model with individual strains and then modeling growth as a function of serotype prevalence, more accurate predictions were obtained.

Development and validation of a predictive model for Listeria monocytogenes Scott A as a function of temperature, pH, and commercial mixture of potassium lactate and sodium diacetate.

The objective of this study was to develop and validate secondary models that can predict growth parameters of L. monocytogenes Scott A as a function of concentrations (0-3%) of a commercial potassium lactate (PL) and sodium diacetate (SDA) mixture, pH (5.5-7.0), and temperature (4-37oC). A total of 120 growth curves were fitted to the Baranyi primary model that directly estimates lag time (LT) and specific growth rate (SGR). The effects of the variables on L. monocytogenes Scott A growth kinetics were modeled by response surface analysis using quadratic and cubic polynomial models of the natural logarithm transformation of both LT and SGR. Model performance was evaluated with dependent data and independent data using the prediction bias (Bf) and accuracy factors (Af) as well as the acceptable prediction zone method [percentage of relative errors (%RE)]. Comparison of predicted versus observed values of SGR indicated that the cubic model fits better than the quadratic model, particularly at 4 and 10oC. The Bf and Af for independent SGR were 1.00 and 1.08 for the cubic model and 1.08 and 1.16 for the quadratic model, respectively. For cubic and quadratic models, the %REs for the independent SGR data were 92.6 and 85.7, respectively. Both quadratic and cubic polynomial models for SGR and LT provided acceptable predictions of L. monocytogenes Scott A growth in the matrix of conditions described in the present study. Model performance can be more accurately evaluated with Bf and Af and % RE together.

Prevalence and antimicrobial resistance of Salmonella recovered from processed poultry.

This study was conducted to determine the prevalence and antimicrobial resistance of Salmonella isolates recovered from processed poultry. Four hundred eighty pre- and postchill whole broiler chicken carcasses were collected from a poultry processing plant between July 2004 and June 2005. Water samples also were collected at the entrance and exit of the chiller. After preenrichment, carcass and water samples were analyzed for the presence of Salmonella using the automated BAX system followed by traditional culture methods. The proportions of pre- and postchill carcasses that were positive for Salmonella were 88.4 and 84.1%, respectively. Ninety-two percent of water samples collected at the entrance of the chiller were positive for Salmonella, but all exit samples were negative. There was no significant difference in the prevalence of Salmonella between pre- and postchill carcasses (P > 0.05). Salmonella isolates recovered were serotyped and tested for susceptibility to antimicrobials. Thirteen serotypes were identified; the most common were Salmonella Kentucky (59.5%) and Salmonella Typhimurium (17.8%). Three hundred thirty-nine (79.8%) of the isolates were resistant to at least one antimicrobial, and 53.4% were resistant to three or more antimicrobials. Resistance was most often observed to tetracycline (73.4% of isolates), ampicillin (52.9%), amoxicillin-clavulanic acid (52%), ceftiofur (51.7%), streptomycin (35.2%), and sulfisoxazole (21.8%). These results indicate the high prevalence of Salmonella contamination in whole broiler carcasses, and a large number of these Salmonella isolates were resistant to commonly used antimicrobials.

A quantitative risk assessment model for Salmonella and whole chickens.

Existing data and predictive models were used to define the input settings of a previously developed but modified quantitative risk assessment model (QRAM) for Salmonella and whole chickens. The QRAM was constructed in an Excel spreadsheet and was simulated using @Risk. The retail-to-table pathway was modeled as a series of unit operations and associated pathogen events that included initial contamination at retail, growth during consumer transport, thermal inactivation during cooking, cross-contamination during serving, and dose response after consumption. Published data as well as predictive models for growth and thermal inactivation of Salmonella were used to establish input settings. Noncontaminated chickens were simulated so that the QRAM could predict changes in the incidence of Salmonella contamination. The incidence of Salmonella contamination changed from 30% at retail to 0.16% after cooking to 4% at consumption. Salmonella growth on chickens during consumer transport was the only pathogen event that did not impact the risk of salmonellosis. For the scenario simulated, the QRAM predicted 0.44 cases of salmonellosis per 100,000 consumers, which was consistent with recent epidemiological data that indicate a rate of 0.66-0.88 cases of salmonellosis per 100,000 consumers of chicken. Although the QRAM was in agreement with the epidemiological data, surrogate data and models were used, assumptions were made, and potentially important unit operations and pathogen events were not included because of data gaps and thus, further refinement of the QRAM is needed.

Development and validation of a tertiary simulation model for predicting the potential growth of Salmonella typhimurium on cooked chicken.

The growth of Salmonella typhimurium (ATCC 14028) on the surface of autoclaved ground chicken breast and thigh burgers incubated at constant temperatures from 8 to 48 degrees C in 2 degrees C increments was investigated and modeled. Growth curves at each temperature were fit to a two-phase linear primary model to determine lag time (lambda) and specific growth rate (mu). Growth of S. typhimurium on breast and thigh meat was not different. Consequently, secondary models that predicted lag time and specific growth rate as a function of temperature were developed with the combined data for breast and thigh meat. Five secondary models for lag time and three secondary models for specific growth rate were compared. A new version of the hyperbola model and a cardinal temperature model were selected as the best secondary models for lag time and specific growth rate, respectively. The secondary models were combined in a computer spreadsheet to create a tertiary simulation model that predicted the potential growth (log10) increase) of S. typhimurium on cooked chicken as a function of time and temperature. Probability distributions and simulation were used in the tertiary model to model the secondary model parameters and the times and temperatures of abuse. The outputs of the tertiary model were validated (prediction bias of -4% for lambda and 1% for mu and prediction accuracy of 10% for lambda and 8% for mu) and integrated with a previously developed risk assessment model for Salmonella.