Predictive Modeling for the Growth ofSalmonellaEnteritidis in Chicken Juice by Real-Time Polymerase Chain Reaction

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.

Modeling the Inactivation, Survival, and Growth of Salmonella enterica under Osmotic Stress Considering Inoculum Phase and Serotype

AIMS

This study evaluated the behaviour of the Salmonella enterica serotypes in osmotically-stressful BHI broth (0.940 ≤ a_w ≤ 0.960), assessing inoculum from two stages of the bacterial life cycle (exponential and stationary) and two temperatures (25 and 35 °C).

METHODS AND RESULTS

Four S. enterica serotypes (Typhimurium, Enteritidis, Heidelberg, and Minnesota) were grown in stressful BHI at 25 °C. A mathematical model was proposed for describing the total microbial count as the sum of two subpopulations, inactivating and surviving-then-growing. When submitted to a_w of 0.950 and 0.960, all strains showed a decreased count, followed by a period of unchanged count and then exponential growth (Phoenix Phenomenon). Strains inoculated at a_w = 0.940 and 0.945 showed inactivation kinetics only. Cells cultivated at 25 °C and inoculated from the exponential phase were the most reactive to the osmotic stress, showing a higher initial population reduction and shorter adaptation period. The proposed model described the inactivation data and the Phoenix Phenomenon accurately.

CONCLUSIONS

The results quantified the complex response of S. enterica to the osmotic environment in detail, depending on the inoculum characteristic and serotype evaluated.

SIGNIFICANCE AND IMPACT OF STUDY

Quantifying these differences is truly relevant to food safety and improves risk analysis.

Characterization of chicken meat contaminated with Salmonella by fluorescence spectroscopy

Contaminated poultry products as eggs and meat are the primary vehicles of Salmonella infection. Conventional methods for microorganisms detections involve multiple steps, and despite its accuracy, these assays are time-consuming. Biosensing methods have shown great potential for the rapid detection of foodborne pathogens. Some of the biosensors are based on fluorescence. Various fluorophores such as collagen, elastin, NAD(P)H, and porphyrins can be used to evaluate possible chemical changes in meat. In this manuscript, the fluorescence properties of chicken meat contaminated with Salmonella enterica (ATCC 14028) cell suspensions (500; 5000; 50,000 and 500,000 cells/mL) were obtained and compared with non-contaminated control, for meat kept at 25 °C for 24 and 48 h. The effects of ambient light were also considered. Our results indicated that free NAD(P)H and coproporphyrin emission bands present in contaminated meat, increased over time, and can provide access to valuable information for the detection of Salmonella in chicken meat.

qPCR quantification of Carnobacterium maltaromaticum, Brochothrix thermosphacta, and Serratia liquefaciens growth kinetics in mixed culture

Quantifying growth kinetics of specific spoilage microorganisms in mixed culture is required to describe the evolution of food microbiomes. A qPCR method was developed to selectively amplify individual meat spoilage bacteria, Carnobacterium maltaromaticum, Brochothrix thermosphacta and Serratia liquefaciens, within a broth medium designed to simulate the composition of beef. An optimized method of DNA extraction was produced for standard curve construction. Method specificity was determined by individual single peaks in melt curves. Reaction efficiency for standard curves of C. maltaromaticum, B. thermosphacta and S. liquefaciens was high (R² = 0.98-0.99), and linear quantification was achieved over a 5 log CFU/ml range. Coefficient of variation was calculated considering both threshold cycle (C_t) and bacterial concentration; the value did not exceed 14% for inter- or intra-runs for either method. Comparison of growth kinetic parameters derived from plate count and qPCR showed no significant variation (P > .05) for growth rate (GR) and maximum population density (MPD); lag phase duration (LPD) was not included in this comparison due to high innate variability. Log quantification of each isolate was validated in a mixed-culture experiment for all three species with qPCR and plate count differing less than 0.3 log CFU/ml (average 0.10 log CFU/ml, R² = 0.98).