Validation of a tertiary model for predicting variation of Salmonella typhimurium DT104 (ATCC 700408) growth from a low initial density on ground chicken breast meat with a competitive microflora

Modeling the growth of Salmonella in raw ground pork under dynamic conditions of temperature abuse

Salmonella contamination of pork products is a significant public health concern. Temperature abuse scenarios, such as inadequate refrigeration or prolonged exposure to room temperature, can enhance Salmonella proliferation. This study aimed to develop and validate models for Salmonella growth considering competition with background microbiota in raw ground pork, under isothermal and dynamic conditions of temperature abuse between 10 and 40 °C. The maximum specific growth rate (μmax) and maximum population density (MPD) were estimated to quantitatively describe the growth behavior of Salmonella. To reflect more realistic microbial interactions in Salmonella-contaminated product, our model considered competition with the background microbiota, measured as mesophilic aerobic plate counts (APC). Notably, the μmax of Salmonella in low-fat samples (∼5 %) was significantly higher (p < 0.05) than that in high-fat samples (∼25 %) at 10, 20, and 30 °C. The average doubling time of Salmonella was 26, 4, 2, 1.5, 0.8, and 1.1 h at 10, 15, 20, 25, 30, and 40 °C, respectively. The initial concentration of Salmonella minimally impacted its growth in ground pork at any temperature. The MPD of APC consistently exceeded that of Salmonella, indicating the growth of APC without competition from Salmonella. The competition model exhibited excellent fit with the experimental data, as 95 % (627/660) of residual errors fell within the desired acceptable prediction zone (pAPZ >0.70). The theoretical minimum and optimum growth temperatures for Salmonella ranged from 5 to 6 °C and 35 to 36 °C, respectively. The dynamic model displayed strong predictive performance, with 90 % (57/63) of residual errors falling within the APZ. Dynamic models could be valuable tools for validating and refining simpler static or isothermal models, ultimately improving their predictive capabilities to enhance food safety.

Predictive modeling of Salmonella spp. growth behavior in cooked and raw chicken samples: Real-time PCR quantification approach and model assessment in different handling scenarios

The increasing prevalence of Salmonella contamination in poultry meat emphasizes the importance of suitable predictive microbiological models for estimating Salmonella growth behavior. This study was conducted to evaluate the potential of chicken juice as a model system to predict the behavior of Salmonella spp. in cooked and raw chicken products and to assess its ability to predict cross-contamination scenarios. A cocktail of four Salmonella serovars was inoculated into chicken juice, sliced chicken, ground chicken, and chicken patties, with subsequent incubation at 10, 15, 20, and 25°C for 39 h. The number of Salmonella spp. in each sample was determined using real-time polymerase chain reaction. Growth curves were fitted into the primary Baranyi and Roberts model to obtain growth parameters. Interactions between temperature and growth parameters were described using the secondary Ratkowsky's square root model. The predictive results generated by the chicken juice model were compared with those obtained from other chicken meat models. Furthermore, the parameters of the chicken juice model were used to predict Salmonella spp. numbers in six worst-case cross-contamination scenarios. Performance of the chicken juice model was evaluated using the acceptable prediction zone from -1.0 (fail-safe) to 0.5 (fail-dangerous) log. Chicken juice model accurately predicted all observed data points within the acceptable range, with the distribution of residuals being wider near the fail-safe zone (75%) than near the fail-dangerous zone (25%). This study offers valuable insights into a novel approach for modeling Salmonella growth in chicken meat products, with implications for food safety through the development of strategic interventions. PRACTICAL APPLICATION: The findings of this study have important implications in the food industry, as chicken juice could be a useful tool for predicting Salmonella behavior in different chicken products and thus reducing the risk of foodborne illnesses through the development of strategic interventions. However, it is important to recognize that some modifications to the chicken juice model will be necessary to accurately mimic all real-life conditions, as multiple factors particularly those related to food processing can vary between different products.

Surveillance of pathogenic bacteria on a food matrix using machine-learning-enabled paper chromogenic arrays

Global food systems can benefit significantly from continuous monitoring of microbial food safety, a task for which tedious operations, destructive sampling, and the inability to monitor multiple pathogens remain challenging. This study reports significant improvements to a paper chromogenic array sensor - machine learning (PCA-ML) methodology sensing concentrations of volatile organic compounds (VOCs) emitted on a species-specific basis by pathogens by streamlining dye selection, sensor fabrication, database construction, and machine learning and validation. This approach enables noncontact, time-dependent, simultaneous monitoring of multiple pathogens (Listeria monocytogenes, Salmonella, and E. coli O157:H7) at levels as low as 1 log CFU/g with over 90% accuracy. The report provides theoretical and practical frameworks demonstrating that chromogenic response, including limits of detection, depends on time integrals of VOC concentrations. The paper also discusses the potential for implementing PCA-ML in the food supply chain for different food matrices and pathogens, with species- and strain-specific identification.

Validation of competition and dynamic models for Shiga toxin-producing Escherichia coli (STEC) growth in raw ground pork during temperature abuse

Epidemiological evidence suggests that pork products may be a vehicle for STEC transmission to humans. This study was conducted to validate competition and dynamic models for the growth of STEC during simulated temperature abuse of raw ground pork. Maximum specific growth rates μmax were modeled as a function of temperature using the Cardinal parameter equation, and a dynamic model was validated using sinusoidal temperature profiles. The Acceptable Prediction Zone (APZ) method was used to evaluate the model's performance. The competition model was well fitted to the experimental data having 93% (1849/1981) residual errors within the desired APZ. Growth rates were not different between STEC O157 and non-O157; however, serogroup O91 showed two to three times lower μmax than other STEC at 10, 25, and 30 °C. The theoretical minimum and optimum growth temperature for all STEC groups ranged from 3.4 to 7.8 °C and 33-35 °C, respectively. The dynamic model showed good prediction performance (pAPZ = 0.98) with the experimental data. These results can be used to inform risk assessment models and to support the implementation of risk mitigation strategies to improve the microbiological safety of raw pork products.

Growth behavior of Shiga toxin-producing Escherichia coli, Salmonella, and generic E. coli in raw pork considering background microbiota at 10, 25, and 40 °C

Recent epidemiological evidence suggests that pork products may be vehicles for the transmission of Shiga toxin-producing Escherichia coli (STEC) to humans. The severe morbidity associated with STEC infections highlights the need for research to understand the growth behavior of these bacteria in pork products. Classical predictive models can estimate pathogen growth in sterile meat. However, competition models considering background microbiota reflect a more realistic scenario for raw meat products. The objective of this study was to estimate the growth kinetics of clinically significant STEC (O157, non-O157, and O91), Salmonella, and generic E. coli in raw ground pork using competition primary growth models at temperature abuse (10 and 25 °C) and sublethal temperature (40 °C). A competition model incorporating the No lag Buchanan model was validated using the acceptable prediction zone (APZ) method where >92 % (1498/1620) of the residual errors fell within the APZ (pAPZ > 0.70). The background microbiota (mesophilic aerobic plate counts, APC) inhibited the growth of STEC and Salmonella indicating a simple one-directional competitive interaction between pathogens and the mesophilic microbiota of ground pork. The maximum specific growth rate (μ_max) of all the bacterial groups was not significantly different (p > 0.05) based on fat content (5 vs 25 %) except for generic E. coli at 10 °C. E. coli O157 and non-O157 behaved similarly in terms of μ_max and maximum population density (MPD). Salmonella showed a similar (p > 0.05) μ_max to E. coli O157 and non-O157 at 10 and 40 °C but a significantly higher rate (p < 0.05) at 25 °C. STEC were more prone to be inhibited by APC than Salmonella at 10 and 25 °C. The μ_max of O91 was lower (p < 0.05) than other STEC and Salmonella at 10 and 25 °C but similar (p > 0.05) at 40 °C. Generic E. coli showed a two- to five-times higher (p < 0.05) μ_max (0.028 ± 0.011 log₁₀ CFU/h) than other bacterial groups (0.006 ± 0.004 to 0.012 ± 0.003 log₁₀ CFU/h) at 10 °C making it a potential indicator bacteria for process control. Industry and regulators can use competitive models to develop appropriate risk assessment and mitigation strategies to improve the microbiological safety of raw pork products.

Mapping foodborne pathogen contamination throughout the conventional and alternative poultry supply chains

Recently, there has been a consumer push for natural and organic food products. This has caused alternative poultry production, such as organic, pasture, and free-range systems, to grow in popularity. Due to the stricter rearing practices of alternative poultry production systems, different types of levels of microbiological risks might be present for these systems when compared to conventional production systems. Both conventional and alternative production systems have complex supply chains that present many different opportunities for flocks of birds or poultry meat to be contaminated with foodborne pathogens. As such, it is important to understand the risks involved during each step of production. The purpose of this review is to detail the potential routes of foodborne pathogen transmission throughout the conventional and alternative supply chains, with a special emphasis on the differences in risk between the two management systems, and to identify gaps in knowledge that could assist, if addressed, in poultry risk-based decision making.

Salmonella Prevalence Alone Is Not a Good Indicator of Poultry Food Safety

Salmonella is a leading cause of foodborne illness (i.e., salmonellosis) outbreaks, which on occasion are attributed to ground turkey. The poultry industry uses Salmonella prevalence as an indicator of food safety. However, Salmonella prevalence is only one of several factors that determine risk of salmonellosis. Consequently, a model for predicting risk of salmonellosis from individual lots of ground turkey as a function of Salmonella prevalence and other risk factors was developed. Data for Salmonella contamination (prevalence, number, and serotype) of ground turkey were collected at meal preparation. Scenario analysis was used to evaluate effects of model variables on risk of salmonellosis. Epidemiological data were used to simulate Salmonella serotype virulence in a dose-response model that was based on human outbreak and feeding trial data. Salmonella prevalence was 26% (n = 100) per 25 g of ground turkey, whereas Salmonella number ranged from 0 to 1.603 with a median of 0.185 log per 25 g. Risk of salmonellosis (total arbitrary units (AU) per lot) was affected (p ≤ 0.05) by Salmonella prevalence, number, and virulence, by incidence and extent of undercooking, and by food consumption behavior and host resistance but was not (p > 0.05) affected by serving size, serving size distribution, or total bacterial load of ground turkey when all other risk factors were held constant. When other risk factors were not held constant, Salmonella prevalence was not correlated (r = -0.39; p = 0.21) with risk of salmonellosis. Thus, Salmonella prevalence alone was not a good indicator of poultry food safety because other factors were found to alter risk of salmonellosis. In conclusion, a more holistic approach to poultry food safety, such as the process risk model developed in the present study, is needed to better protect public health from foodborne pathogens like Salmonella.

The Effect of Previous Life Cycle Phase on the Growth Kinetics, Morphology, and Antibiotic Resistance of Salmonella Typhimurium DT104 in Brain Heart Infusion and Ground Chicken Extract

Growth models are predominately used in the food industry to estimate the potential growth of selected microorganisms under environmental conditions. The growth kinetics, cellular morphology, and antibiotic resistance were studied throughout the life cycle of Salmonella Typhimurium. The effect of the previous life cycle phase [late log phase (LLP), early stationary phase (ESP), late stationary phase (LSP), and early death phase (EDP)] of Salmonella after reinoculation in brain heart infusion broth (BHI), ground chicken extract (GCE), and BHI at pH 5, 7, and 9 and salt concentrations 2, 3, and 4% was investigated. The growth media and previous life cycle phase had significant effects on the lag time (λ), specific growth rate (μ max), and maximum population density (Y max). At 2 and 4% salt concentration, the LLP had the significantly (p < 0.05) fastest μ max (1.07 and 0.69 log CFU/ml/h, respectively). As the cells transitioned from the late log phase (LLP) to the early death phase (EDP), the λ significantly (p < 0.05) increased. At pH 5 and 9, the EDP had a significantly (p < 0.05) lower Y max than the LLP, ESP, and LSP. As the cells transitioned from a rod shape to a coccoid shape in the EDP, the cells were more susceptible to antibiotics. The cells regained their resistance as they transitioned back to a rod shape from the EDP to the log and stationary phase. Our results revealed that growth kinetics, cell's length, shape, and antibiotic resistance were significantly affected by the previous life cycle phase. The results of this study also demonstrate that the previous life cycle should be considered when developing growth models of foodborne pathogens to better ensure the safety of poultry and poultry products.

Risk of Salmonellosis from Chicken Parts Prepared from Whole Chickens Sold in Flow Pack Wrappers and Subjected to Temperature Abuse

The flow pack wrapper is a popular packaging choice for retail sale of whole chickens. However, it may provide a favorable environment for growth and spread of Salmonella within the package, leading to an outbreak of salmonellosis. To investigate this possibility, a process risk model was developed that predicted the risk of salmonellosis from chicken parts prepared from whole chickens sold in flow pack wrappers and subjected to proper storage (6 h at 4°C) or improper storage (72 h at 15°C) before preparation. The model had four unit operations (pathogen events): (i) preparation (contamination), (ii) cooking (death), (iii) serving (cross-contamination), and (iv) consumption (dose-response). Data for prevalence, number, and serotype of Salmonella on chicken parts were obtained by whole sample enrichment, real-time PCR. Improper storage increased (P < 0.05) prevalence of Salmonella on raw chicken parts from 10.6% (17 of 160) to 41.2% (66 of 160) and incidence of cross-contamination of cooked chicken from 10% (4 of 40) to 52.2% (24 of 46). Improper storage also increased (P < 0.05) the number (mean ± standard deviation) of Salmonella from 0.017 ± 0.030 to 3.51 ± 1.34 log per raw chicken part and from 0.048 ± 0.089 to 3.08 ± 1.50 log per cooked chicken part. The predominant serotypes isolated (n = 111) were Typhimurium (34.2%), Typhimurium var 5- (20.7%), Kentucky (12.6%), Enteritidis (11.7%), and Heidelberg (8.1%). When chicken was properly stored before preparation, the model predicted that risk of salmonellosis was low and sporadic with only six cases per 100 simulations of 10⁵ chicken parts. However, when 0.1 to 1% of chickens were improperly stored before preparation, the model predicted that salmonellosis would increase (P < 0.05) linearly from a median of 7 (range, 1 to 15) to a median of 72 (range, 52 to 93) cases per 10⁵ chicken parts. These results indicated that the flow pack wrapper provided a favorable environment for growth and spread of Salmonella within the package and that even when only a small percentage of packages were subjected to improper storage before preparation, the risk and size of an outbreak of salmonellosis from chicken parts increased significantly.

Neural Network Model for Survival and Growth of Salmonella enterica Serotype 8,20:-:z6 in Ground Chicken Thigh Meat during Cold Storage: Extrapolation to Other Serotypes

Mathematical models that predict the behavior of human bacterial pathogens in food are valuable tools for assessing and managing this risk to public health. A study was undertaken to develop a model for predicting the behavior of Salmonella enterica serotype 8,20:-:z6 in chicken meat during cold storage and to determine how well the model would predict the behavior of other serotypes of Salmonella stored under the same conditions. To develop the model, ground chicken thigh meat (0.75 cm(3)) was inoculated with 1.7 log Salmonella 8,20:-:z6 and then stored for 0 to 8 -8 to 16°C. An automated miniaturized most-probable-number (MPN) method was developed and used for the enumeration of Salmonella. Commercial software (Excel and the add-in program NeuralTools) was used to develop a multilayer feedforward neural network model with one hidden layer of two nodes. The performance of the model was evaluated using the acceptable prediction zone (APZ) method. The number of Salmonella in ground chicken thigh meat stayed the same (P > 0.05) during 8 days of storage at -8 to 8°C but increased (P < 0.05) during storage at 9°C (+0.6 log) to 16°C (+5.1 log). The proportion of residual values (observed minus predicted values) in an APZ (pAPZ) from -1 log (fail-safe) to 0.5 log (fail-dangerous) was 0.939 for the data (n = 426 log MPN values) used in the development of the model. The model had a pAPZ of 0.944 or 0.954 when it was extrapolated to test data (n = 108 log MPN per serotype) for other serotypes (S. enterica serotype Typhimurium var 5-, Kentucky, Typhimurium, and Thompson) of Salmonella in ground chicken thigh meat stored for 0 to 8 days at -4, 4, 12, or 16°C under the same experimental conditions. A pAPZ of ≥0.7 indicates that a model provides predictions with acceptable bias and accuracy. Thus, the results indicated that the model provided valid predictions of the survival and growth of Salmonella 8,20:-:z6 in ground chicken thigh meat stored for 0 to 8 days at -8 to 16°C and that the model was validated for extrapolation to four other serotypes of Salmonella.

Validation of a predictive model for survival and growth of Salmonella typhimurium DT104 on chicken skin for extrapolation to a previous history of frozen storage

The U.S. Department of Agriculture's tertiary Pathogen Modeling Program (PMP) model for survival and growth of Salmonella enterica ser. Typhimurium definitive type 104 (DT104) on chicken skin stored for 0 to 8 h at 5 to 50°C was evaluated for its ability to predict survival and growth of the same organism on chicken skin after frozen storage for 6 days at -20°C. Experimental design and methods used to collect data for model development (dependent data) were the same as those used to collect data for survival and growth after frozen storage (independent data for extrapolation). This was done to provide a valid comparison of observed and predicted values. The model was classified as providing acceptable predictions of the test data 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 pAPZ for dependent data, independent data for interpolation, and independent data for extrapolation to a new independent variable of previous frozen storage were all acceptable (pAPZ ≥0.7), with the exception of the pAPZ for dependent data at 50°C, where an unacceptable pAPZ of 0.625 was obtained. Although a majority of observed log counts were less than predicted log counts, indicating that frozen storage of chicken skin for 6 days at -20°C had injured some Salmonella Typhimurium DT104, the injury was not large enough to cause the tertiary PMP model to provide unacceptable predictions. Thus, it was concluded that the tertiary PMP model provided valid predictions of survival and growth of Salmonella Typhimurium DT104 on chicken skin that had a previous history of frozen storage for 6 days at -20°C. Additional research is needed to determine how broadly the model can be applied to other conditions of previous frozen storage.

Quantitative risk assessment of human salmonellosis in Canadian broiler chicken breast from retail to consumption

The current quantitative risk assessment model followed the framework proposed by the Codex Alimentarius to provide an estimate of the risk of human salmonellosis due to consumption of chicken breasts which were bought from Canadian retail stores and prepared in Canadian domestic kitchens. The model simulated the level of Salmonella contamination on chicken breasts throughout the retail-to-table pathway. The model used Canadian input parameter values, where available, to represent risk of salmonellosis. From retail until consumption, changes in the concentration of Salmonella on each chicken breast were modeled using equations for growth and inactivation. The model predicted an average of 318 cases of salmonellosis per 100,000 consumers per year. Potential reasons for this overestimation were discussed. A sensitivity analysis showed that concentration of Salmonella on chicken breasts at retail and food hygienic practices in private kitchens such as cross-contamination due to not washing cutting boards (or utensils) and hands after handling raw meat along with inadequate cooking contributed most significantly to the risk of human salmonellosis. The outcome from this model emphasizes that responsibility for protection from Salmonella hazard on chicken breasts is a shared responsibility. Data needed for a comprehensive Canadian Salmonella risk assessment were identified for future research.

Growth and inactivation of Salmonella at low refrigerated storage temperatures and thermal inactivation on raw chicken meat and laboratory media: mixed effect meta-analysis

Growth and inactivation regression equations were developed to describe the effects of temperature on Salmonella concentration on chicken meat for refrigerated temperatures (⩽10 °C) and for thermal treatment temperatures (55–70 °C). The main objectives were: (i) to compare Salmonella growth/inactivation in chicken meat versus laboratory media; (ii) to create regression equations to estimate Salmonella growth in chicken meat that can be used in quantitative risk assessment (QRA) modeling; and (iii) to create regression equations to estimate D-values needed to inactivate Salmonella in chicken meat. A systematic approach was used to identify the articles, critically appraise them, and pool outcomes across studies. Growth represented in density (Log₁₀ CFU/g) and D-values (min) as a function of temperature were modeled using hierarchical mixed effects regression models. The current meta-analysis analysis found a significant difference (P ⩽ 0.05) between the two matrices – chicken meat and laboratory media – for both growth at refrigerated temperatures and inactivation by thermal treatment. Growth and inactivation were significantly influenced by temperature after controlling for other variables; however, no consistent pattern in growth was found. Validation of growth and inactivation equations against data not used in their development is needed.

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.

Survival of Salmonella strains differing in their biofilm-formation capability upon exposure to hydrochloric and acetic acid and to high salt

Acidic and osmotic treatments are part of hurdle systems to control pathogens such as Salmonella in food. In the current study, Salmonella enterica isolates previously shown to differ in their ability to form biofilms were grown in diluted tryptic soy broth (TSB) (1:5 dilution in distilled water) and subsequently exposed to phosphate-buffered saline (PBS) adjusted to pH 3.0 with HCl, PBS adjusted to pH 3.9 with acetic acid or rice vinegar diluted 1:15 with distilled water (pH 3.9). Cells grown in diluted TSB were also exposed to distilled water, pH 7.6, containing 5 M NaCl. No differences in survival upon exposure to PBS adjusted to pH 3.0 with HCl or distilled water containing high salt were observed between the isolates; however, exposure to acetic acid and rice vinegar resulted in lower survival levels of isolates previously shown to be poor biofilm formers. The numbers (log(10) cfu/ml) of surviving cells after exposure for 36 hr to acetic acid and rice vinegar were 4.43 ± 0.24 vs. 2.27 ± 0.87 (P<0.05) and 5.19 ± 0.12 vs. 2.33 ± 0.93 (P<0.05) for isolates with a high vs. low biofilm-forming ability. The survival data could be fitted with the Weibull model. The data suggest that the ability of Salmonella strains to survive in the presence of acetic acid and rice vinegar parallels their ability to form biofilms. Thus, Salmonella with a high biofilm-formation capability might be more difficult to kill with acetic acid found in foods or cleaning solutions.

Effect of native microflora on the growth kinetics of salmonella enteritidis strain 04-137 in raw ground chicken

Effects of native microflora (NM) on growth kinetics of Salmonella Enteritidis strain 04-137 were studied in raw ground chicken. First, samples of ground chicken with high and low levels of NM (10(7.1) and 10(4.9) CFU/g, respectively) were spiked with Salmonella at doses ranging from 10(1) to 10(4) CFU/g. The growth kinetics, including the rate constant of growth, r, and the lag period, were similar, but the maximum cell level, N(max), was higher at higher initial Salmonella doses for both NM levels. Second, samples of ground chicken with high and low NM levels (10(6.8) and 10(4.7) CFU/g, respectively) were spiked with Salmonella and then stored at various constant temperatures ranging from 8 to 32°C. Both N(max) and r for Salmonella were higher at higher temperatures for both NM levels. Although r for total bacteria, which consisted of NM and Salmonella, was also higher at higher temperatures, N(max) was constant at all temperatures for both NM levels. Further, Salmonella growth was compared among samples of ground chicken with high and low NM levels and samples of sterilized chicken. Salmonella growth, characterized by both N(max) and r, was highest in sterilized chicken, followed by chicken with the low NM level. Our growth model successfully described and analyzed the growth of Salmonella and total bacteria in chicken at constant temperatures; using the data obtained, the model also successfully predicted the growth of Salmonella and total bacteria in chicken stored at dynamic temperatures. Our study clarified the effects that different doses of NM in ground chicken had on the growth kinetics of the Salmonella strain and demonstrated the usability of the growth model for foods with NM.

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.

A random effect multiplicative heteroscedastic model for bacterial growth

BACKGROUND

Predictive microbiology develops mathematical models that can predict the growth rate of a microorganism population under a set of environmental conditions. Many primary growth models have been proposed. However, when primary models are applied to bacterial growth curves, the biological variability is reduced to a single curve defined by some kinetic parameters (lag time and growth rate), and sometimes the models give poor fits in some regions of the curve. The development of a prediction band (from a set of bacterial growth curves) using non-parametric and bootstrap methods permits to overcome that problem and include the biological variability of the microorganism into the modelling process.

RESULTS

Absorbance data from Listeria monocytogenes cultured at 22, 26, 38, and 42 degrees C were selected under different environmental conditions of pH (4.5, 5.5, 6.5, and 7.4) and percentage of NaCl (2.5, 3.5, 4.5, and 5.5). Transformation of absorbance data to viable count data was carried out. A random effect multiplicative heteroscedastic model was considered to explain the dynamics of bacterial growth. The concept of a prediction band for microbial growth is proposed. The bootstrap method was used to obtain resamples from this model. An iterative procedure is proposed to overcome the computer intensive task of calculating simultaneous prediction intervals, along time, for bacterial growth. The bands were narrower below the inflection point (0-8 h at 22 degrees C, and 0-5.5 h at 42 degrees C), and wider to the right of it (from 9 h onwards at 22 degrees C, and from 7 h onwards at 42 degrees C). A wider band was observed at 42 degrees C than at 22 degrees C when the curves reach their upper asymptote. Similar bands have been obtained for 26 and 38 degrees C.

CONCLUSIONS

The combination of nonparametric models and bootstrap techniques results in a good procedure to obtain reliable prediction bands in this context. Moreover, the new iterative algorithm proposed in this paper allows one to achieve exactly the prefixed coverage probability for the prediction band. The microbial growth bands reflect the influence of the different environmental conditions on the microorganism behaviour, helping in the interpretation of the biological meaning of the growth curves obtained experimentally.

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.

Predictive model for survival and growth of Salmonella typhimurium DT104 on chicken skin during temperature abuse

To better predict risk of Salmonella infection from chicken subjected to temperature abuse, a study was undertaken to develop a predictive model for survival and growth of Salmonella Typhimurium DT104 on chicken skin with native flora. For model development, chicken skin portions (2.14 cm2) were inoculated with 0.85 log of Salmonella Typhimurium DT104 (ATCC 700408) and then stored at 5 to 50 degrees C for 8 h. Kinetic data from the storage trials were fit to a primary model to determine lag time (lamda), specific growth rate (micrro), and the 95% prediction interval (PI). Secondary models for lamda, mu, and PI as a function of storage temperature were developed and then combined with the primary model to create a tertiary model. Performance of the tertiary model was evaluated against dependent data, independent data for interpolation, and independent data for extrapolation to kosher chicken skin by using an acceptable prediction zone from -1 (fail-safe) to 0.5 (fail-dangerous) log per skin portion. Survival of Salmonella Typhimurium DT104 on chicken skin was observed during 8 h of storage at 5 to 20 degrees C and at 50 degrees C, whereas growth was observed from 25 to 45 degrees C and was optimal at 40 degrees C with a lamda of 2.5 h and a mu of 1.1 log/h. Variation of pathogen growth, as assessed by PI, increased in a nonlinear manner as a function of temperature and was greater for growth conditions than no-growth conditions. The percentage of acceptable prediction errors was 82.6% for dependent data, 83.7% for independent data for interpolation, and 81.6% for independent data for extrapolation to kosher skin, which all exceeded the performance criterion of 70% acceptable predictions. Thus, it was concluded that the tertiary model provided valid predictions for survival and growth of Salmonella Typhimurium DT104 from a low initial dose on both nonkosher and kosher chicken skin with native flora.

Modeling the growth of Salmonella in raw poultry stored under aerobic conditions

The presence of Salmonella in raw poultry is a well-recognized risk factor for foodborne illness. The objective of this study was to develop and validate a mathematical model that predicts the growth of Salmonella in raw poultry stored under aerobic conditions at a variety of temperatures. One hundred twelve Salmonella growth rates were extracted from 12 previously published studies. These growth rates were used to develop a square-root model relating the growth rate of Salmonella to storage temperature. Model predictions were compared to growth rate measurements collected in our laboratory for four poultry-specific Salmonella strains (two antibiotic-resistant and two nonresistant strains) inoculated onto raw chicken tenderloins. Chicken was inoculated at two levels (10(3) CFU/cm2 and < or = 10 CFU/cm2) and incubated at temperatures ranging from 10 to 37 degrees C. Visual inspection of the data, bias and accuracy factors, and comparison with two other published models were used to analyze the performance of the new model. Neither antibiotic resistance nor inoculum size affected Salmonella growth rates. The presence of spoilage microflora did not appear to slow the growth of Salmonella. Our model provided intermediate predicted growth rates when compared with the two other published models. Our model predicted slightly faster growth rates than those observed in inoculated chicken in the temperature range of 10 to 28 degrees C but slightly slower growth rates than those observed between 30 and 37 degrees C. Slightly negative bias factors were obtained in every case (-5 to -3%); however, application of the model may be considered fail-safe for storage temperatures below 28 degrees C.

An approach for mapping the number and distribution of Salmonella contamination on the poultry carcass

Mapping the number and distribution of Salmonella on poultry carcasses will help guide better design of processing procedures to reduce or eliminate this human pathogen from poultry. A selective plating media with multiple antibiotics (xylose-lysine agar medium [XL] containing N-(2-hydroxyethyl)piperazine-N'-(2-ethanesulfonic acid) and the antibiotics chloramphenicol, ampicillin, tetracycline, and streptomycin [XLH-CATS]) and a multiple-antibiotic-resistant strain (ATCC 700408) of Salmonella Typhimurium definitive phage type 104 (DT104) were used to develop an enumeration method for mapping the number and distribution of Salmonella Typhimurium DT104 on the carcasses of young chickens in the Cornish game hen class. The enumeration method was based on the concept that the time to detection by drop plating on XLH-CATS during incubation of whole chicken parts in buffered peptone water would be inversely related to the initial log number (N0) of Salmonella Typhimurium DT104 on the chicken part. The sampling plan for mapping involved dividing the chicken into 12 parts, which ranged in average size from 36 to 80 g. To develop the enumeration method, whole parts were spot inoculated with 0 to 6 log Salmonella Typhimurium DT104, incubated in 300 ml of buffered peptone water, and detected on XLH-CATS by drop plating. An inverse relationship between detection time on XLH-CATS and N0 was found (r = -0.984). The standard curve was similar for the individual chicken parts and therefore, a single standard curve for all 12 chicken parts was developed. The final standard curve, which contained a 95% prediction interval for providing stochastic results for N0, had high goodness of fit (r2 = 0.968) and was N0 (log) = 7.78 +/- 0.61 - (0.995 x detention time). Ninety-five percent of N0 were within +/- 0.61 log of the standard curve. The enumeration method and sampling plan will be used in future studies to map changes in the number and distribution of Salmonella on carcasses of young chickens fed the DT104 strain used in standard curve development and subjected to different processing procedures.

Development and validation of a stochastic model for predicting the growth of Salmonella typhimurium DT104 from a low initial density on chicken frankfurters with native microflora

The presence of native microflora is associated with increased variation of Salmonella growth among batches and portions of chicken meat and as a function of temperature. However, variation of Salmonella growth can be modeled using a 95% prediction interval (PI). Because there are no reports of predictive models for growth of Salmonella on ready-to-eat poultry meat products with native microflora and because Salmonella is usually present at low levels on poultry meat, the current study was conducted to develop and validate a stochastic model for predicting the growth of Salmonella from a low initial density on chicken frankfurters with native microflora. One-gram portions of chicken frankfurters were inoculated with 0.5 log CFU of a single strain (ATCC 700408) of Salmonella Typhimurium DT104. Changes in pathogen numbers over time, N(t), were fit to a two-phase linear primary model to determine lag time (lambda), growth rate (mu), and the 95% PI, which characterized the variation of pathogen growth. Secondary quadratic polynomial models for natural log transformations of lambda, mu, and PI as a function of temperature (10 to 40 degrees C) were obtained by nonlinear regression. The primary and secondary models were combined in a computer spreadsheet to create a tertiary model that predicted the growth curve and PI. The pathogen did not grow on chicken frankfurters incubated at 10 to 12 degrees C, but mu ranged from 0.003 log CFU/g/h at 14 degrees C to 0.176 log CFU/ g/h at 30 degrees C to 0.1 log CFU/g/h at 40 degrees C. Variation of N(t) increased as a function of time (i.e., PI was lower during lag phase than during growth phase) and temperature (i.e., PI was higher at 18 to 40 degrees C than at 10 to 14 degrees C). For dependent data (n = 338), 90.5% of observed N(t) values were in the PI predicted by the tertiary model, whereas for independent data (n = 86), 89.5% of observed N(t) values were in the PI predicted by the tertiary model. Based on this performance evaluation, the tertiary model was considered acceptable and valid for stochastic predictions of Salmonella Typhimurium DT104 growth from a low initial density on chicken frankfurters with native microflora.

Number of Salmonella on chicken breast filet at retail level and its implications for public health risk

This study aimed to characterize the number of Salmonella on chicken breast filet at the retail level and to evaluate if this number affects the risk of salmonellosis. From October to December 2005, 220 chilled raw filets (without skin) were collected from five local retail outlets in The Netherlands. Filet rinses that were positive after enrichment were enumerated with a three-tube most-probable-number (MPN) assay. Nineteen filets (8.6%) were contaminated above the detection limit of the MPN method (10 Salmonella per filet). The number of Salmonella on positive filets varied from 1 to 3.81 log MPN per filet. The obtained enumeration data were applied in a risk assessment model. The model considered possible growth during domestic storage, cross-contamination from filet via a cutting board to lettuce, and possible illness due to consumption of the prepared lettuce. A screening analysis with expected-case and worst-case estimates for the input values of the model showed that variability in the inputs was of relevance. Therefore, a Monte Carlo simulation with probability distributions for the inputs was carried out to predict the annual number of illnesses. Remarkably, over two-thirds of annual predicted illnesses were caused by the small fraction of filets containing more than 3 log Salmonella at retail (0.8% of all filets). The enumeration results can be used to confirm this hypothesis in a more elaborate risk assessment. Modeling of the supply chain can provide insight for possible intervention strategies to reduce the incidence of rare, but extreme levels. Reduction seems feasible within current practices, because the retail market study indicated a significant difference between suppliers.

Validation of a tertiary model for predicting variation of Salmonella typhimurium DT104 (ATCC 700408) growth from a low initial density on ground chicken breast meat with a competitive microflora

Growth of a multiple antibiotic-resistant strain (ATCC 700408) of Salmonella Typhimurium definitive phage type 104 (DT104) from a low initial density (10(0.6) most probable number [MPN] or CFU/g) on ground chicken breast meat with a competitive microflora was investigated and modeled as a function of time and temperature (10 to 40 degrees C). MPN and viable counts (CFU) on a selective medium with four antibiotics enumerated the pathogen. Data from five replicate challenge studies per temperature were combined and fit to a primary model to determine maximum specific growth rate (micro), maximum population density (Nmax), and the 95% prediction interval (PI). Nonlinear regression was used to obtain secondary models as a function of temperature for micro, Nmax, and PI, which ranged from 0.04 to 0.4 h(-1), 1.6 to 9.4 log MPN or CFU/g, and 1.4 to 2.4 log MPN or CFU/g, respectively. Secondary models were combined with the primary model to create a tertiary model for predicting variation (95% PI) of pathogen growth among batches of ground chicken breast meat with a competitive microflora. The criterion for acceptable model performance was that 90% of observed MPN or CFU data had to be in the 95% PI predicted by the tertiary model. For data (n=344) used in model development, 93% of observed MPN and CFU data were in the 95% PI predicted by the tertiary model, whereas for data (n=236) not used in model development but collected using the same methods, 94% of observed MPN and CFU data were in the 95% PI predicted by the tertiary model. Thus, the tertiary model was successfully verified against dependent data and validated against independent data for predicting variation of Salmonella Typhimurium DT104 growth among batches of ground chicken breast meat with a competitive microflora and from a low initial density.