One-step kinetic analysis of competitive growth of Salmonella spp. and background flora in ground chicken

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.

Survival and growth behavior of common foodborne pathogens in falafel paste under different storage temperatures

Falafel is a popular breakfast food in the Middle East that has been recently involved in several outbreaks of foodborne illnesses. The aim of the study was to explore the growth behavior of Salmonella enterica, Escherichia coli O157:H7, Shigella sonnie, Shigella flexneri, Listeria monocytogenes and Staphylococcus aureus in falafel paste (FP) under different storage temperatures (4, 10, or 24 °C) for 14 days. FP (pH = 6.2, aw = 0.96) was inoculated with 5.0 to 6.0 log CFU/g of each of the pathogens separately. Salmonella spp. significantly declined by 1.5 log at 4 °C but grew significantly by ca. 2 and 4 log at 10 and 24 °C, respectively after 14 days. E. coli O157:H7 significantly increased (4.5 log) in FP when stored under 24 °C and survived at a level of ~105 CFU/g at 10 °C. Comparatively, Sh. sonnie and Sh. flexneri showed a better survival pattern in FP stored under 4 °C and grew (˃ 3 log) after 5 days at 10 and 24 °C. L. monocytogenes was capable of growing by 1.9 and 4.3 log after 14 d days and by 3.9 log after 3 days at 4, 10, or 24 °C, respectively. No significant decline in S. aureus counts at 4 and 10 °C occurred, however, it increased significantly to ˃ 7 log CFU/g at 24 °C. Total mesophilic count and yeast and mold count reached to spoilage levels (˃107 CFU/g) in un-inoculated FP after 1 and 3 days of storage at 24 and 10 °C, respectively. FP could support the growth of common foodborne pathogens and hence it is recommended to utilize natural antimicrobials in FP and keep the product under refrigeration (4 °C) to preclude the growth of vegetative foodborne pathogens.

Establishment and Application of a Predictive Growth Kinetic Model of Salmonella with the Appearance of Two Other Dominant Background Bacteria in Fresh Pork

To better guide microbial risk management and control, growth kinetic models of Salmonella with the coexistence of two other dominant background bacteria in pork were constructed. Sterilized pork cutlets were inoculated with a cocktail of Salmonella Derby (S. Derby), Pseudomonas aeruginosa (P. aeruginosa), and Escherichia coli (E. coli), and incubated at various temperatures (4-37 °C). The predictive growth models were developed based on the observed growth data. By comparing R² of primary models, Baranyi models were preferred to fit the growth curves of S. Derby and P. aeruginosa, while the Huang model was preferred for E. coli (all R² ≥ 0.997). The secondary Ratkowsky square root model can well describe the relationship between temperature and μ_max (all R² ≥ 0.97) or Lag (all R² ≥ 0.98). Growth models were validated by the actual test values, with B_f and A_f close to 1, and MSE around 0.001. The time for S. Derby to reach a pathogenic dose (10⁵ CFU/g) at each temperature in pork was predicted accordingly and found to be earlier than the time when the pork began to be judged nearly fresh according to the sensory indicators. Therefore, the predictive microbiology model can be applied to more accurately predict the shelf life of pork to secure its quality and safety.

One- and Two-Step Kinetic Data Analysis Applied for Single and Co-Culture Growth of Staphylococcus aureus, Escherichia coli, and Lactic Acid Bacteria in Milk

The objective of this study was to compare one- and two-step kinetic data analysis approaches to describe the growth of Staphylococcus aureus, Escherichia coli, and lactic acid bacteria Fresco 1010 starter culture in milk under isothermal conditions between 10 and 37 °C. The primary Huang model (HM) and secondary square root model were applied to lag times and growth rates of each of the population. The one-step approach for single cultures data enabled the direct construction of a tertiary model combining primary and secondary models to determine parameters from all growth data, thus minimizing the transfer of errors from one model to another. The statistical indices showed a significant improvement in the prediction capability provided by this approach. Then, a one-step approach combining the primary Huang, Giménez, and Dalgaard model (H-GD) with the secondary square root model was used to simultaneously model the growth of the populations mentioned above in co-culture under the same conditions. Independent isothermal data sets were chosen for validation of the growth description of single cultures (HM) and co-culture (H-GD) using validation factors, including the bias (Bf) and accuracy (Af). For example, the values of Af for the one-step approach range from 1.17 to 1.20 and 1.04 to 1.08 for single cultures and co-culture, respectively, demonstrating high accuracy. Thus, this approach may be used for co-culture growth description in general or specifically, e.g., in various types of lactic acid fermentation, including artisanal cheese-making technology.