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.

[Analysis of Maximum Growth Rate and Construction of Predictive Growth Model for Bacillus cereusin Mashed Potato by Calorimetric Method].

The maximum growth rate (µmax) of Bacillus cereus was estimated using a non-destructive isothermal calorimetric method, and a growth prediction model was constructed based on the measurement results. SCD medium and mashed potato were inoculated with serial-diluted inoculum of B. cereus. Heat generation curves were determined using an isothermal calorimeter at 35, 25, and 15℃. The µmax was determined from the relationship between the increase in B. cereus cell number and incubation time, which was detected through the heat generation of the B. cereus biological process. Moreover, the growth prediction model was constructed using Ratkowsky's square-root model. The results of the growth prediction model based on the data of the calorimetric and conventional culture methods for SCD were expressed as √µCalmax=0.0354 (T-4.9)[R2=0.99] and √µCCMmax=0.0335 (T-5.0)[R2=0.99]; a similar equation was provided by both methods. Conversely, the results of the growth prediction model based on the calorimetric method data for mashed potato were given as √µCalmax=0.0390 (T-8.5)[R2=0.99]; the maximum growth rates at 30 and 20℃ were predicted as 0.70 and 0.20 (1/hr), respectively. The maximum growth rates obtained using the conventional culture method were 0.63 and 0.29 (1/hr), respectively, similar to the calorimetric method results. The predictive microbiological analysis using the calorimetric method enabled the rapid provision of a growth prediction equation, and the number of samples could be substantially reduced compared with that for the conventional culture method.

Predictive Growth Model of Listeria monocytogenes Under Fluctuating Temperature Conditions in Pasteurized Milk by Using Real-Time Polymerase Chain Reaction.

The aim of this study was to evaluate the application of real-time polymerase chain reaction (PCR)-based quantification as a rapid and accurate tool for the monitoring and prediction of Listeria monocytogenes growth in pasteurized milk under constant and fluctuating temperature conditions. The growth of L. monocytogenes was monitored under constant temperature conditions at 4°C, 10°C, 15°C, 20°C, and 35°C. High correlation was obtained between the bacterial growth rate and incubation temperature, where the R2 of the slope of the square root model was calculated to be 0.993 and 0.996 for real-time PCR and the conventional culture method, respectively. Moreover, the obtained maximum specific growth rate (µmax) data plots were correlated with 188 L. monocytogenes µmax data points from the existing model according to ComBase database, with an R2 of 0.961 for real-time PCR and of 0.931 for the conventional culture method. The growth models were examined under three different patterns of fluctuating temperature conditions ranging from 2°C to 30°C. The prediction results fell within ±20% of the relative error zone, showing that real-time PCR quantification could be used for fast, sensitive, and specific bacterial growth monitoring with high-throughput results. Real-time PCR should be considered a promising option and powerful tool for the construction of a bacterial growth prediction model for safety risk analysis in the dairy industry.

Inactivation of Escherichia coli O157 and Salmonella Enteritidis in raw beef liver by gamma irradiation.

Irradiation of ground beef and beef liver inoculated with Escherichia coli O157 466 and DT66 and Salmonella Enteritidis 3313 were performed with gamma rays from cobalt-60 at refrigerated and frozen temperatures under air- and vacuum-packaged conditions. Results showed that D10 values for all pathogens in frozen beef liver were higher than those in frozen ground beef samples, with significant differences observed between the D10 values of E. coli O157 466 and S. Enteritidis 3313 under air-packaged conditions, as well as in E. coli O157 DT66 and S. Enteritidis 3313 under vacuum-packaged conditions. To verify effective bacterial inactivation under high bacterial-contamination levels (105-107 CFU/g), survival/death interfaces of E. coli O157 DT66 and S. Enteritidis 3313 inoculated in beef liver under vacuum-packaged and frozen conditions were constructed, with results suggesting that doses from 5.3 kGy to 5.5 kGy and 8.2 kGy-8.5 kGy would be sufficient to kill 105 CFU/g of E. coli O157 and S. Enteritidis 3313, respectively, at a 95%-99% predicted confidence interval. These results suggested that food matrixes containing high amounts of antioxidants (such as beef liver) and treated under frozen and vacuum-packaged conditions require additional consideration and evaluation for applications of irradiation treatment.

Predictive Modeling for the Growth of Salmonella Enteritidis in Chicken Juice by Real-Time Polymerase Chain Reaction.

The goals of this study were to monitor the growth kinetics of Salmonella Enteritidis in chicken juice using real-time polymerase chain reaction (PCR) and to evaluate its efficacy by comparing the results with an experimental database. Salmonella Enteritidis was inoculated in chicken juice samples at an initial inoculum of 104 CFU/mL with inoculated samples incubated at six different temperatures (10, 15, 20, 25, 30, and 35°C). Sampling was carried out for 36 h to observe the growth of Salmonella Enteritidis. The total DNA was extracted from the samples, and the copy number of the Salmonella invasion gene (invA) was quantified by real-time PCR and converted to Salmonella Enteritidis cell concentration. Growth kinetics data were analyzed by the Baranyi and Roberts model to obtain growth parameters, whereas the Ratkowsky's square-root model was used to describe the effect of the interactions between growth parameters and temperature on the growth of Salmonella Enteritidis. The growth parameters of Salmonella Enteritidis obtained from an experiment conducted at a constant temperature were validated with growth data from chicken juice samples that were incubated under fluctuating temperature conditions between 5°C and 30°C for 30-min periods. A high correlation was observed between maximum growth rate (µmax) and storage temperature, indicating that the real-time PCR-monitoring method provides a precise estimation of Salmonella Enteritidis growth in food material with a microbial flora. Moreover, the µmax data reflected data from microbial responses viewer database and ComBase. The results of this study suggested that real-time PCR monitoring provides a precise estimation of Salmonella Enteritidis growth in food materials with a background microbial flora.