Estimating parametric distributions of storage time and temperature of ready-to-eat foods for U.S. households

Assessment of the exposure to cytotoxic Bacillus cereus group genotypes through HTST milk consumption

This study addresses the limited tools available for assessing food safety risks from cytotoxic Bacillus cereus group strains in contaminated food. We quantified the growth, in skim milk broth, of 17 cytotoxic B. cereus strains across 6 phylogenetic groups with various virulence gene profiles. The strains did not grow in HTST milk at 4 or 6°C. At 10°C, 15 strains exhibited growth; at 8°C, one strain grew; and all strains grew at temperatures ≥ 14°C. Using growth data from 16 strains, we developed linear secondary growth models and an exposure assessment model. This model, simulating a 5-stage HTST milk supply chain and up to 35 d of consumer storage with an initial contamination of 100 cfu/mL, estimated that 2.81 ± 0.66% and 4.13 ± 2.53% of milk containers would surpass 105 cfu/mL of B. cereus by d 21 and 35, respectively. A sensitivity analysis identified the initial physiological state of cells (Q0) as the most influential variable affecting predictions for specific isolates. What-if scenarios indicated that increases in mean and variability of consumer storage temperatures significantly affected the predicted B. cereus concentrations in milk. This model serves as an initial tool for risk-based food safety decision making regarding low-level B. cereus contamination.

Strain-specific Growth Parameters are Important to Accurately Model Bacterial Growth on Baby Spinach in Simulation Models

Digital tools to predict produce shelf life have the potential to reduce food waste and improve consumer satisfaction. To address this need, we (i) performed an observational study on the microbial quality of baby spinach, (ii) completed growth experiments of bacteria that are representative of the baby spinach microbiota, and (iii) developed an initial simulation model of bacterial growth on baby spinach. Our observational data showed that the predominant genera found on baby spinach were Pseudomonas, Pantoea and Exiguobacterium. Rifampicin-resistant mutants (rifR mutants) of representative bacterial subtypes were subsequently generated to obtain strain-specific growth parameters on baby spinach. These experiments showed that: (i) it is difficult to select rifR mutants that do not have fitness costs affecting growth (9 of 15 rifR mutants showed substantial differences in growth, compared to their corresponding wild-type strain) and (ii) based on estimates from primary growth models, the mean (geometric) maximum population of rifR mutants on baby spinach (7.6 log10 CFU/g, at 6°C) appears lower than that of the spinach microbiota (9.6 log10 CFU/g, at 6°C), even if rifR mutants did not have substantial growth-related fitness costs. Thus, a simulation model, parameterized with the data obtained here as well as literature data on home refrigeration temperatures, underestimated bacterial growth on baby spinach. The root mean square error of the simulation's output, compared against data from the observational study, was 1.11 log10 CFU/g. Sensitivity analysis was used to identify key parameters (e.g., strain maximum population) that impact the simulation model's output, allowing for prioritization of future data collection to improve the simulation model. Overall, this study provides a roadmap for the development of models to predict bacterial growth on leafy vegetables with strain-specific parameters and suggests that additional data are required to improve these models.

Development and deployment of a supply-chain digital tool to predict fluid-milk spoilage due to psychrotolerant sporeformers

Psychrotolerant sporeformers pose a challenge to maintaining fluid milk quality. Dynamic temperature changes along the supply chain can favor the germination and growth of these bacteria and lead to fluid milk spoilage. In this study, we aim to expand on our previous work on predicting milk spoilage due to psychrotolerant sporeformers. The key model innovations include (1) the ability to account for changing temperatures along the supply chain, and (2) a deployed user-friendly interface to allow easy access to the model. Using the frequencies and concentrations of 8 Bacillales subtypes specific to fluid milk collected in New York, the model simulated sporeformer growth in half-gallons of high-temperature, short-time (HTST) pasteurized fluid milk transported from processing facility to retail store and then to consumer. The Monte Carlo simulations predicted that 44.3% of half-gallons of milk were spoiled (defined as having a bacterial concentration >20,000 cfu/mL, a conservative estimate that represents the Pasteurized Milk Ordinance regulatory limit) after 21 d of refrigerated storage at consumer's home. Model validations showed that the model was the most accurate in predicting the mean sporeformer concentration at low temperatures (i.e., at 3°C and 4°C; compared with higher temperatures at 6°C and 10°C) within the first 21 d of consumer storage, with a root mean square error of 0.29 and 0.34 log10 cfu/mL, respectively. Global sensitivity analyses indicated that home storage temperature, facility-to-retail transportation temperature, and initial spore concentration were the 3 most influential factors for predicting milk spoilage on d 21 of shelf life. What-if scenarios indicated that microfiltration was predicted to be the most effective strategy to reduce spoilage. The implementation of this strategy (assumed to reduce initial spore concentration by 2.2 log10 cfu/mL) was predicted to reduce the percentage of spoiled milk by 17.0 percentage points on d 21 of storage and could delay the date by which 50% of half-gallons of milk were spoiled, from d 25 to 35. Overall, the model is readily deployed as a digital tool for assessing fluid milk spoilage along the supply chain and evaluating the effectiveness of intervention strategies, including those that target storage temperatures at different supply chain stages.

Seafood dose parameters: Updating 210Po retention factors for cooking, decay loss and mariculture

210Po has been identified as one of the main contributors to ingestion doses to humans, particularly from the consumption of seafood. The amount of 210Po activity concentration data for various types of seafood has increased greatly in recent times. However, to provide realistic seafood dose assessments, most 210Po data requires correction to account for losses that can occur before the seafood is actually consumed. Here we develop generic correction factors for the main processes associated with reduction of 210Po in seafood - leaching during cooking, radioactive decay between harvest and consumption, and sourcing from mariculture versus wild-caught. When seafood is cooked, the overall mean fraction of 210Po retained is 0.74 for all cooking and seafood types, with the means for various seafood types and cooking categories ranging from 0.56 to 1.03. When considering radioactive decay during the period between harvest and consumption, the overall mean fraction remaining is 0.81 across all seafood preservation/packaging types, with estimates ranging from 0.50 (canned seafood) to 0.98 (fresh seafood). Regarding mariculture influence, the available limited data suggest marine fish and crustaceans raised with processed feed have about one order of magnitude lower (×0.10) 210Po muscle content than wild-caught seafood of the same or similar species, although this ratio varies. Overall, this study concludes that 210Po activity concentrations in seafood at the time of ingestion may be reduced to only about 55% compared to when it was harvested. Therefore, correction factors must be applied to any data derived from environmental monitoring in order to achieve realistic dose estimates. The data also suggest lower 210Po ingestion doses for consumers who routinely favour cooked, long shelf-life and farmed fish/crustaceans. However, more data is needed in some categories, especially for cooking of molluscs and seaweed, and for the 210Po content in all farmed seafood.

Ready-to-eat Meat Plant Characteristics Associated with Food Safety Deficiencies During Regulatory Compliance Audits, Ontario, Canada

Food safety deficiencies in ready-to-eat (RTE) meat processing plants can increase foodborne disease risks. The purpose of this study was to identify common deficiencies and factors related to improved food safety performance in RTE meat plants in Ontario. Routine food safety audit records for licensed provincial free-standing meat processing plants (FSMPs) and abattoirs that process RTE meats were obtained and analyzed in Ontario, Canada, from 2015 to 2019. A Bayesian regression analysis was conducted to examine the association between selected plant characteristics and two outcomes: overall audit rating (pass vs. conditional pass or fail) and individual audit item fail rate. The audit rating was examined in a logistic model, while the audit item fail rate was evaluated in a negative binomial model. The majority (87.7%, n = 800/912) of audits resulted in a pass rating (compared to conditional pass or fail). The mean number of employees per plant, among 200/204 plants with employee data available, was 11.6 (SD = 20.6, range = 1-200). For the logistic regression model, FSMPs were predicted to have a much higher probability of passing audits than abattoirs (32.0% on average, with a 95% credible interval [CI] of 13.8-52.8%). The number of plant employees, water source (municipal vs. private), and types of RTE meat products produced had little to no consistent association with this outcome. The negative binomial model predicted a -0.009 points lower fail rate, on average, for audit items among FSMPs than abattoirs (95% CI: -0.001, -0.018). Meat plants producing jerky had a higher audit item fail rate compared to those that did not produce such products. The other investigated variables had little to no association with this outcome. The results found in this study can support and guide future inspection, audit and outreach efforts to reduce foodborne illness risks associated with RTE meats.

Low-cost, on-farm intervention to reduce spores in bulk tank raw milk benefits producers, processors, and consumers

Bacterial spores, which are found in raw milk, can survive harsh processing conditions encountered in dairy manufacturing, including pasteurization and drying. Low-spore raw milk is desirable for dairy industry stakeholders, especially those who want to extend the shelf life of their product, expand their distribution channels, or reduce product spoilage. A recent previous study showed that an on-farm intervention that included washing towels with chlorine bleach and drying them completely, as well as training milking parlor employees to focus on teat end cleaning, significantly reduced spore levels in bulk tank raw milk. As a follow up to that previous study, here we calculate the costs associated with that previously described intervention as ranging from $9.49 to $13.35 per cow per year, depending on farm size. A Monte Carlo model was used to predict the shelf life of high temperature, short time fluid milk processed from raw milk before and after this low-cost intervention was applied, based on experimental data collected in a previous study. The model predicted that 18.24% of half-gallon containers of fluid milk processed from raw milk receiving no spore intervention would exceed the pasteurized milk ordinance limit of 20,000 cfu/mL by 17 d after pasteurization, while only 16.99% of containers processed from raw milk receiving the spore intervention would reach this level 17 d after pasteurization (a reduction of 1.25 percentage points and a 6.85% reduction). Finally, a survey of consumer milk use was conducted to determine how many consumers regularly consume fluid milk near or past the date printed on the package (i.e., code date), which revealed that over 50% of fluid milk consumers surveyed continue to consume fluid milk after this date, indicating that a considerable proportion of consumers are exposed to fluid milk that is likely to have high levels spore-forming bacterial growth and possibly associated quality defects (e.g., flavor or odor defects). This further highlights the importance of reducing spore levels in raw milk to extend pasteurized fluid milk shelf life and thereby reducing the risk of adverse consumer experiences. Processors who are interested in extending fluid milk shelf life by controlling the levels of spores in the raw milk supply should consider incentivizing low-spore raw milk through premium payments to producers.

Quantitative risk assessment model to investigate the public health impact of varying Listeria monocytogenes allowable levels in different food commodities: A retrospective analysis

Invasive listeriosis is a potentially fatal foodborne disease that according to this study may affect up to 32.9 % of the US population considered as increased risk and including people with underlying conditions and co-morbidities. Listeria monocytogenes has been scrutinized in research and surveillance programs worldwide in Ready-to-Eat (RTE) food commodities (RTE salads, deli meats, soft/semi-soft cheese, seafood) and frozen vegetables in the last 30 years with an estimated overall prevalence of 1.4-9.9 % worldwide (WD) and 0.5-3.8 % in the United States (US). Current L. monocytogenes control efforts have led to a prevalence reduction in the last 5 years of 4.9-62.9 % (WD) and 12.4-92.7 % (US). A quantitative risk assessment model was developed, estimating the probability of infection in the US susceptible population to be 10-10,000× higher than general population and the total number of estimated cases in the US was 1044 and 2089 cases by using the FAO/WHO and Pouillot dose-response models. Most cases were attributed to deli meats (>90 % of cases) followed by RTE salads (3.9-4.5 %), soft and semi-soft cheese and RTE seafood (0.5-1.0 %) and frozen vegetables (0.2-0.3 %). Cases attributed to the increased risk population corresponded to 96.6-98.0 % of the total cases with the highly susceptible population responsible for 46.9-80.1 % of the cases. Removing product lots with a concentration higher than 1 CFU/g reduced the prevalence of contamination by 15.7-88.3 % and number of cases by 55.9-100 %. Introducing lot-by-lot testing and defining allowable quantitative regulatory limits for low-risk RTE commodities may reduce the public health impact of L. monocytogenes and improve the availability of enumeration data.

Functional Genomics Identified Novel Genes Involved in Growth at Low Temperatures in Listeria monocytogenes

Listeria monocytogenes (Lm) is a foodborne pathogen that can cause severe human illness. Standard control measures for restricting bacterial growth, such as refrigeration, are often inadequate as Lm grows well at low temperatures. To identify genes involved in growth at low temperatures, a powerful functional genomics method Tn-seq was performed in this study. This genome-wide screening comprehensively identified the known and novel genetic determinants involved in low-temperature growth. A novel gene lmo1366, encoding rRNA methyltransferase, was identified to play an essential role in Lm growth at 16°C. In contrast, the inactivation of lmo2301, a gene encoding the terminase of phage A118, significantly enhanced the growth of Lm at 16°C. The deletion of lmo1366 or lmo2301 resulted in cell morphology alterations and impaired the growth rate in milk and other conditions at low temperatures. Transcriptomic analysis revealed that the Δlmo1366 and Δlmo2301 mutants exhibited altered transcriptional patterns compared to the wild-type strain at 16°C with significant differences in genes involved in ribosome structural stability and function, and membrane biogenesis, respectively. This work uncovered novel genetic determinants involved in Lm growth at 16°C, which could lead to a better understanding of how bacteria survive and multiply at low temperatures. Furthermore, these findings could potentially contribute to developing novel antibacterial strategies under low-temperature conditions.

IMPORTANCEListeria monocytogenes is a Gram-positive pathogen that contributes to foodborne outbreaks due to its ability to survive at low temperatures. However, the genetic determinants of Lm involved in growth at low temperatures have not been fully defined. Here, the genetic determinants involved in the low-temperature growth of Lm were comprehensively identified on a genome-wide scale by Tn-seq. The gene lmo1366, encoding rRNA methyltransferase, was identified essential for growth under low-temperature conditions. On the other hand, the gene lmo2301, encoding terminase of phage A118, plays a negative role in bacterial growth at low temperatures. The transcriptomic analysis revealed the potential mechanisms. These findings lead to a better understanding of how bacteria survive and multiply at low temperatures and could provide unique targets for novel antibacterial strategies under low-temperature conditions.

Development of a Monte Carlo simulation model to predict pasteurized fluid milk spoilage due to post-pasteurization contamination with gram-negative bacteria

Psychrotolerant gram-negative bacteria introduced as post-pasteurization contamination (PPC) are a major cause of spoilage and reduced shelf life of high-temperature, short-time pasteurized fluid milk. To provide improved tools to (1) predict pasteurized fluid milk shelf life as influenced by PPC and (2) assess the effectiveness of different potential interventions that could reduce spoilage due to PPC, we developed a Monte Carlo simulation model that predicts fluid milk spoilage due to psychrotolerant gram-negative bacteria introduced as PPC. As a first step, 17 gram-negative bacterial isolates frequently associated with fluid milk spoilage were selected and used to generate growth data in skim milk broth at 6°C. The resulting growth parameters, frequency of isolation for the 17 different isolates, and initial concentration of bacteria in milk with PPC, were used to develop a Monte Carlo model to predict bacterial number at different days of shelf life based on storage temperature of milk. This model was then validated with data from d 7 and 10 of shelf life, collected from commercial operations. The validated model predicted that the parameters (1) maximum growth rate and (2) storage temperature had the greatest influence on the percentage of containers exceeding 20,000 cfu/mL standard plate count on d 7 and 10 (i.e., spoiling due to PPC), which indicates that accurate data on maximum growth rate and storage temperature are important for accurate predictions. In addition to allowing for prediction of fluid milk shelf life, the model allows for simulation of "what-if" scenarios, which allowed us to predict the effectiveness of different interventions to reduce overall fluid milk spoilage due to PPC through a set of proof-of-concept scenario (e.g., frequency of PPC in containers reduced from 100% to 10%; limiting distribution temperature to a maximum of 6°C). Combined with other models, such as previous models on fluid milk spoilage due to psychrotolerant spore-forming bacteria, the data and tools developed here will allow for rational, digitally enabled, fluid milk shelf life prediction and quality enhancement.

Assessment of microbial risk during Australian industrial practices for Escherichia coli O157:H7 in fresh cut-cos lettuce: A stochastic quantitative approach

Escherichia coli O157:H7 risk associated with the consumption of fresh cut-cos lettuce during Australian industrial practices was assessed. A probabilistic risk assessment model was developed and implemented in the @Risk software by using the Monte Carlo simulation technique with 1,000,000 iterations. Australian preharvest practices yielded predicted annual mean E. coli O157:H7 levels from 0.2 to -3.4 log CFU/g and prevalence values ranged from 2 to 6.4%. While exclusion of solar radiation from the baseline model yielded a significant increase in concentration of E. coli O157:H7 (-5.2 -log fold), drip irrigation usage, exclusion of manure amended soil and rainfall reduced E. coli O157:H7 levels by 7.4, 6.5, and 4.3-log fold, respectively. The microbial quality of irrigation water and irrigation type both had a significant effect on E. coli O157:H7 concentrations at harvest (p < 0.05). The probability of illness due to consumption of E. coli O157:H7 contaminated fresh cut-cos lettuce when water washing interventions were introduced into the processing module, was reduced by 1.4-2.7-log fold (p < 0.05). This study provides a robust basis for assessment of risk associated with E. coli O157:H7 contamination on fresh cut-cos lettuce for industrial practices and will assist the leafy green industry and food safety authorities in Australia to identify potential risk management strategies.

Evaluation of public health risk for Escherichia coli O157:H7 in cilantro

This study sought to model the growth and die-off of Escherichia coli (E. coli) O157:H7 along the cilantro supply chain from farm-to-fork to investigate its risk to public health. Contributing factors included in the model were on farm contamination from irrigation water and soil, solar radiation, harvesting, and transportation and storage times and temperatures. The developed risk model estimated the microbiological risks associated with E. coli O157:H7 in cilantro and determined the parameters that had the most effect on the estimated number of illnesses per year so future mitigation strategies could be applied. Results showed a similar decrease in the E. coli O157:H7 (median values) concentrations along the supply chain for cilantro grown in both winter and summer weather conditions. With an estimated 0.1% prevalence of E. coli O157:H7 contamination for cilantro post-harvest used for illustration, the model predicted the probability of illness from consuming fresh cilantro as very low with fewer than two illnesses per every one billion servings of cilantro (1.6 × 10^-9; 95th percentile). Although rare, 3.7% and 1.6% of scenarios run in this model for summer and winter grown cilantro, respectively, result in over 10 cases per year in the United States. This is reflected in real life where illnesses from cilantro are seen rarely but outbreaks have occurred. Sensitivity analysis and scenario testing demonstrated that ensuring clean and high quality irrigation water and preventing temperature abuse during transportation from farm to retail, are key to reducing overall risk of illness.

An Assessment of Listeriosis Risk Associated with a Contaminated Production Lot of Frozen Vegetables Consumed under Alternative Consumer Handling Scenarios

Frozen foods do not support the growth of Listeria monocytogenes (LM) and should be handled appropriately for safety. However, consumer trends regarding preparation of some frozen foods may contribute to the risk of foodborne listeriosis, specifically when cooking instructions are not followed and frozen products are instead added directly to smoothies or salads. A quantitative microbial risk assessment model FFLLoRA (Frozen Food Listeria Lot Risk Assessment) was developed to assess the lot-level listeriosis risk due to LM contamination in frozen vegetables consumed as a ready-to-eat food. The model was designed to estimate listeriosis risk per serving and the number of illnesses per production lot of frozen vegetables contaminated with LM, considering individual facility factors such as lot size, prevalence of LM contamination, and consumer handling prior to consumption. A production lot of 1 million packages with 10 servings each was assumed. When at least half of the servings were cooked prior to consumption, the median risk of invasive listeriosis per serving in both the general and susceptible population was <1.0 × 10^-16 with the median (5th, 95th percentiles) predicted number of illnesses per lot as 0 (0, 0) and 0 (0, 1) under the exponential and Weibull-gamma dose-response functions, respectively. In scenarios in which all servings are consumed as ready-to-eat, the median predicted risk per serving was 1.8 × 10^-13 and 7.8 × 10^-12 in the general and susceptible populations, respectively. The median (5th, 95th percentile) number of illnesses was 0 (0, 0) and 0 (0, 6) for the exponential and Weibull-Gamma models, respectively. Classification tree analysis highlighted initial concentration of LM in the lot, temperature at which the product is thawed, and whether a serving is cooked as main predictors for illness from a lot. Overall, the FFLLoRA provides frozen food manufacturers with a tool to assess LM contamination and consumer behavior when managing rare and/or minimal contamination events in frozen foods.

The Network Source Location Problem in the Context of Foodborne Disease Outbreaks

In today’s globally interconnected food system, outbreaks of foodborne disease can spread widely and cause considerable impact on public health. Food distribution is a complex system that can be seen as a network of trade flows connecting supply chain actors. Identifying the source of an outbreak of foodborne disease distributed across this network can be solved by considering this network structure and the dimensions of information it contains. The literature on the network source identification problem has grown widely in recent years covering problems in many different contexts, from contagious disease infecting a human population, to computer viruses spreading through the Internet, to rumors or trends diffusing through a social network. Much of this work has focused on studying this problem in analytically tractable frameworks, designing approaches to work on trees and extending to general network structures in an ad hoc manner. These simplified frameworks lack many features of real-world networks and problem contexts that can dramatically impact transmission dynamics, and therefore, backwards inference of the transmission process. Moreover, the features that distinguish foodborne disease in the context of source identification have not previously been studied or identified. In this article we identify these features, then provide a review of existing work on the network source identification problem, categorizing approaches according to these features. We conclude that much of the existing work cannot be implemented in the foodborne disease problem because it makes assumptions about the transmission process that are unrealistic in the context of food supply networks—that is, identifying the source of an epidemic contagion whereas foodborne contamination spreads through a transport network-mediated diffusion process, or because it requires data that is not available—complete observations of the contamination status of all nodes in the network.

Quantitative Microbial Risk Assessment for Escherichia coli O157:H7 in Fresh-Cut Lettuce

Leafy green vegetables, including lettuce, are recognized as potential vehicles for foodborne pathogens such as Escherichia coli O157:H7. Fresh-cut lettuce is potentially at high risk of causing foodborne illnesses, as it is generally consumed without cooking. Quantitative microbial risk assessments (QMRAs) are gaining more attention as an effective tool to assess and control potential risks associated with foodborne pathogens. This study developed a QMRA model for E. coli O157:H7 in fresh-cut lettuce and evaluated the effects of different potential intervention strategies on the reduction of public health risks. The fresh-cut lettuce production and supply chain was modeled from field production, with both irrigation water and soil as initial contamination sources, to consumption at home. The baseline model (with no interventions) predicted a mean probability of 1 illness per 10 million servings and a mean of 2,160 illness cases per year in the United States. All intervention strategies evaluated (chlorine, ultrasound and organic acid, irradiation, bacteriophage, and consumer washing) significantly reduced the estimated mean number of illness cases when compared with the baseline model prediction (from 11.4- to 17.9-fold reduction). Sensitivity analyses indicated that retail and home storage temperature were the most important factors affecting the predicted number of illness cases. The developed QMRA model provided a framework for estimating risk associated with consumption of E. coli O157:H7-contaminated fresh-cut lettuce and can guide the evaluation and development of intervention strategies aimed at reducing such risk.

Listeria monocytogenes in Retail Delicatessens: An Interagency Risk Assessment-Risk Mitigations

Cross-contamination, improper holding temperatures, and insufficient sanitary practices are known retail practices that may lead to product contamination and growth of Listeria monocytogenes. However, the relative importance of control options to mitigate the risk of invasive listeriosis from ready-to-eat (RTE) products sliced or prepared at retail is not well understood. This study illustrates the utility of a quantitative risk assessment model described in a first article of this series (Pouillot, R., D. Gallagher, J. Tang, K. Hoelzer, J. Kause, and S. B. Dennis, J. Food Prot. 78:134-145, 2015) to evaluate the public health impact associated with changes in retail deli practices and interventions. Twenty-two mitigation scenarios were modeled and evaluated under six different baseline conditions. These scenarios were related to sanitation, worker behavior, use of growth inhibitors, cross-contamination, storage temperature control, and reduction of the level of L. monocytogenes on incoming RTE food products. The mean risk per serving of RTE products obtained under these scenarios was then compared with the risk estimated in the baseline condition. Some risk mitigations had a consistent impact on the predicted listeriosis risk in all baseline conditions (e.g. presence or absence of growth inhibitor), whereas others were greatly dependent on the initial baseline conditions or practices in the deli (e.g. preslicing of products). Overall, the control of the bacterial growth and the control of contamination at its source were major factors of listeriosis risk in these settings. Although control of cross-contamination and continued sanitation were also important, the decrease in the predicted risk was not amenable to a simple solution. Findings from these predictive scenario analyses are intended to encourage improvements to retail food safety practices and mitigation strategies to control L. monocytogenes in RTE foods more effectively and to demonstrate the utility of quantitative risk assessment models to inform risk management decisions.

Food Consumption and Handling Survey for Quantitative Microbiological Consumer Phase Risk Assessments

In the consumer phase of a typical quantitative microbiological risk assessment (QMRA), mathematical equations identify data gaps. To acquire useful data we designed a food consumption and food handling survey (2,226 respondents) for QMRA applications that is especially aimed at obtaining quantitative data. For a broad spectrum of food products, the survey covered the following topics: processing status at retail, consumer storage, preparation, and consumption. Questions were designed to facilitate distribution fitting. In the statistical analysis, special attention was given to the selection of the most adequate distribution to describe the data. Bootstrap procedures were used to describe uncertainty. The final result was a coherent quantitative consumer phase food survey and parameter estimates for food handling and consumption practices in The Netherlands, including variation over individuals and uncertainty estimates.

Comparing listeriosis risks in at-risk populations using a user-friendly quantitative microbial risk assessment tool and epidemiological data

Although infection by the pathogenic bacterium Listeria monocytogenes is relatively rare, consequences can be severe, with a high case-fatality rate in vulnerable populations. A quantitative, probabilistic risk assessment tool was developed to compare estimates of the number of invasive listeriosis cases in vulnerable Canadian subpopulations given consumption of contaminated ready-to-eat delicatessen meats and hot dogs, under various user-defined scenarios. The model incorporates variability and uncertainty through Monte Carlo simulation. Processes considered within the model include cross-contamination, growth, risk factor prevalence, subpopulation susceptibilities, and thermal inactivation. Hypothetical contamination events were simulated. Results demonstrated varying risk depending on the consumer risk factors and implicated product (turkey delicatessen meat without growth inhibitors ranked highest for this scenario). The majority (80%) of listeriosis cases were predicted in at-risk subpopulations comprising only 20% of the total Canadian population, with the greatest number of predicted cases in the subpopulation with dialysis and/or liver disease. This tool can be used to simulate conditions and outcomes under different scenarios, such as a contamination event and/or outbreak, to inform public health interventions.

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.

Listeria monocytogenes in Retail Delicatessens: an Interagency Risk Assessment-model and baseline results

The Interagency Risk Assessment-Listeria monocytogenes (Lm) in Retail Delicatessens provides a scientific assessment of the risk of listeriosis associated with the consumption of ready-to-eat (RTE) foods commonly prepared and sold in the delicatessen (deli) of a retail food store. The quantitative risk assessment (QRA) model simulates the behavior of retail employees in a deli department and tracks the Lm potentially present in this environment and in the food. Bacterial growth, bacterial inactivation (following washing and sanitizing actions), and cross-contamination (from object to object, from food to object, or from object to food) are evaluated through a discrete event modeling approach. The QRA evaluates the risk per serving of deli-prepared RTE food for the susceptible and general population, using a dose-response model from the literature. This QRA considers six separate retail baseline conditions and provides information on the predicted risk of listeriosis for each. Among the baseline conditions considered, the model predicts that (i) retail delis without an environmental source of Lm (such as niches), retail delis without niches that do apply temperature control, and retail delis with niches that do apply temperature control lead to lower predicted risk of listeriosis relative to retail delis with niches and (ii) retail delis with incoming RTE foods that are contaminated with Lm lead to higher predicted risk of listeriosis, directly or through cross-contamination, whether the contaminated incoming product supports growth or not. The risk assessment predicts that listeriosis cases associated with retail delicatessens result from a sequence of key events: (i) the contaminated RTE food supports Lm growth; (ii) improper retail and/or consumer storage temperature or handling results in the growth of Lm on the RTE food; and (iii) the consumer of this RTE food is susceptible to listeriosis. The risk assessment model, therefore, predicts that cross-contamination with Lm at retail predominantly results in sporadic cases.

Preliminary stochastic model for managing Vibrio parahaemolyticus and total viable bacterial counts in a Pacific oyster (Crassostrea gigas) supply chain

Vibrio parahaemolyticus can accumulate and grow in oysters stored without refrigeration, representing a potential food safety risk. High temperatures during oyster storage can lead to an increase in total viable bacteria counts, decreasing product shelf life. Therefore, a predictive tool that allows the estimation of both V. parahaemolyticus populations and total viable bacteria counts in parallel is needed. A stochastic model was developed to quantitatively assess the populations of V. parahaemolyticus and total viable bacteria in Pacific oysters for six different supply chain scenarios. The stochastic model encompassed operations from oyster farms through consumers and was built using risk analysis software. Probabilistic distributions and predictions for the percentage of Pacific oysters containing V. parahaemolyticus and high levels of viable bacteria at the point of consumption were generated for each simulated scenario. This tool can provide valuable information about V. parahaemolyticus exposure and potential control measures and can help oyster companies and regulatory agencies evaluate the impact of product quality and safety during cold chain management. If coupled with suitable monitoring systems, such models could enable preemptive action to be taken to counteract unfavorable supply chain conditions.

Microbiological safety of domestic refrigerators and the dishcloths used to clean them in Guadalajara, Jalisco, Mexico

Household refrigerators are a potential pathogen contamination source for foods. An evaluation of the microbiological safety of 200 refrigerators in Guadalajara, Jalisco, Mexico, was made by visual inspection, ATP-bioluminescence levels, indicator microorganisms including Escherichia coli and Staphylococcus aureus, and the presence of Listeria monocytogenes and Salmonella. Additionally, interviews of the owners of the refrigerators were carried out to determine relationships between food storage practices, demographic aspects, and microbiological status. Dishcloths used to clean refrigerators were also analyzed. Operational conditions (cleanliness, fullness, organization, frequency of cleaning, and temperature) were evaluated by trained observers. Results showed deficient cleanliness in 55% of refrigerators, 22% were completely full, 43% very disorganized, 28% were usually cleaned only once in 3 to 6 months, and 53% had internal temperatures >7.1°C. ATP-bioluminescence levels were >300 relative light units on 67 and 74% of shelves and drawers, respectively, indicating that surfaces were dirty according to the luminometer manufacturer. Psychrotrophic aerobic bacteria counts on shelves, drawers, and dishcloths were 6.3, 5.2, and 6.3 log CFU/cm(2); for coliform bacteria, 5.2, 3.9, and 4.7 CFU/cm(2); for E. coli, 3.7, 3.5, and 4.8 CFU/cm(2); and for Staphylococcus aureus, 2.1, 2.5, and 2.3 CFU/cm(2), respectively. L. monocytogenes and Salmonella were isolated from 59.5, 20.5, and 17% and 32.5, 8.0 and 12.5% of shelves, drawers, and dishcloths, respectively. Four Salmonella serotypes and nine serogroups (partially serotyped isolates) were identified. The most prevalent were Salmonella Anatum (39.5%), Salmonella group E1 (19.7%), and Salmonella group E1 monophasic (12.5%). Operational conditions and microbiological status were clearly deficient in sampled refrigerators, highlighting the consequent risk of foodborne disease among users. Educational programs are needed to improve the domestic food safety in Mexico.

Comparison of public health impact of Listeria monocytogenes product-to-product and environment-to-product contamination of deli meats at retail

This study compared the relative public health impact in deli meats at retail contaminated with Listeria monocytogenes by either (i) other products or (ii) the retail environment. Modeling was performed using the risk of listeriosis-associated deaths as a public health outcome of interest and using two deli meat products (i.e., ham and turkey, both formulated without growth inhibitors) as model systems. Based on reported data, deli meats coming to retail were assumed to be contaminated at a frequency of 0.4%. Three contamination scenarios were investigated: (i) a baseline scenario, in which no additional cross-contamination occurred at retail, (ii) a scenario in which an additional 2.3% of products were cross-contaminated at retail due to transfer of L. monocytogenes cells from already contaminated ready-to-eat deli meats, and (iii) a scenario in which an additional 2.3% of products were contaminated as a result of cross-contamination from a contaminated retail environment. By using a previously reported L. monocytogenes risk assessment model that uses product-specific growth kinetic parameters, cross-contamination of deli ham and turkey was estimated to increase the relative risk of listeriosis-associated deaths by 5.9- and 6.1-fold, respectively, for contamination from other products and by 4.9- and 5.8-fold, respectively, for contamination from the retail environment. Sensitivity and scenario analyses indicated that the frequency of cross-contamination at retail from any source (other food products or environment) was the most important factor affecting the relative risk of listeriosis-associated deaths. Overall, our data indicate that retail-level cross-contamination of ready-to-eat deli meats with L. monocytogenes has the potential to considerably increase the risk of human listeriosis cases and deaths, and thus precise estimates of cross-contamination frequency are critical for accurate risk assessments.

Quantitative assessment of the microbial risk of leafy greens from farm to consumption: preliminary framework, data, and risk estimates

This project was undertaken to relate what is known about the behavior of Escherichia coli O157:H7 under laboratory conditions and integrate this information to what is known regarding the 2006 E. coli O157:H7 spinach outbreak in the context of a quantitative microbial risk assessment. The risk model explicitly assumes that all contamination arises from exposure in the field. Extracted data, models, and user inputs were entered into an Excel spreadsheet, and the modeling software @RISK was used to perform Monte Carlo simulations. The model predicts that cut leafy greens that are temperature abused will support the growth of E. coli O157:H7, and populations of the organism may increase by as much a 1 log CFU/day under optimal temperature conditions. When the risk model used a starting level of -1 log CFU/g, with 0.1% of incoming servings contaminated, the predicted numbers of cells per serving were within the range of best available estimates of pathogen levels during the outbreak. The model predicts that levels in the field of -1 log CFU/g and 0.1% prevalence could have resulted in an outbreak approximately the size of the 2006 E. coli O157:H7 outbreak. This quantitative microbial risk assessment model represents a preliminary framework that identifies available data and provides initial risk estimates for pathogenic E. coli in leafy greens. Data gaps include retail storage times, correlations between storage time and temperature, determining the importance of E. coli O157:H7 in leafy greens lag time models, and validation of the importance of cross-contamination during the washing process.