Validation software tool (ValT) for predictive microbiology based on the acceptable prediction zones method

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.

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.

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 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 Zones (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.

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.

Food safety and food security through predictive microbiology tools: a short review

This article discusses the issues of food safety and food security as a matter of global health. Foodborne illness and deaths caused by pathogens in food continue to be a worldwide problem, with a reported 600 million cases per year, leading to around 420,000 deaths in 2010. Predictive microbiology can play a crucial role in ensuring safe food through mathematical modelling to estimate microbial growth and behaviour. Food security is described as the social and economical means of accessing safe and nutritious food that meets people's dietary preferences and requirements for an active and healthy life. The article also examines various factors that influence food security, including economic, environmental, technological, and geopolitical challenges globally. The concept of food safety is described as a science-based process or action that prevents food from containing substances that could harm human health. Food safety receives limited attention from policymakers and consumers in low- and middle-income countries, where food safety issues are most prevalent. The article also highlights the importance of detecting contaminants and pathogens in food to prevent foodborne illnesses and reduce food waste. Food and Agriculture Organization (FAO), an institution belonging to World Health Organization (WHO) presented calls to action to solve some of the emerging problems in food safety, as it should be a concern of all people to be involved in the pursue of safer food. The guarantee of safe food pertaining to microbiological contamination, as there are different types of active microorganisms in foods, could be obtained using predictive microbiology tools, which study and analyse different microorganisms' behaviour through mathematical models. Studies published by several authors show the application of primary, secondary, or tertiary models of predictive microbiology used for different food products.