Knowledge, Health, and Social Drivers of Frozen Vegetable Consumption Practices Relevant to Listeriosis in Women of Childbearing Age.

In recent years, there have been numerous recalls of frozen vegetable products due to Listeria monocytogenes contamination, which causes listeriosis. In pregnant women, listeriosis can cause miscarriage, stillbirth, and other serious complications. Manufacturing guidelines are created with the intention that frozen vegetables will be cooked prior to consumption. However, consumers may prepare and eat frozen vegetables without prior cooking. Therefore, it is necessary to assess behaviors that could be risky for L. monocytogenes exposure. A 10-question online survey was distributed to women between the ages of 18-54 to investigate frozen vegetable consumption behaviors. The prevalence of uncooked frozen vegetable consumption, reading preparation instructions, and listeriosis knowledge was assessed. Data were analyzed using logistic and ordered logit regression. Of 1,001 complete responses, 531 (53%) indicated that they consumed frozen vegetables in the past week, and of those 35.6% (n = 189) indicated that they consumed frozen vegetables without prior heating. Women who had not heard of listeriosis and had not read preparation instructions had significantly higher odds of uncooked frozen vegetable consumption (Odds Ratio (OR): 2.30, 95% Confidence Interval (CI): 1.48, 3.55; OR: 1.85, 95% CI: 1.13, 3.01, respectively). These results will guide future research on safe food handling practices for frozen vegetable products. The findings support the need for updating public health guidelines to include frozen vegetables as foods that are risky for listeriosis in pregnancy. Additionally, these findings have implications for future research to inform food policy governing labeling regulation on frozen vegetable products to reflect current consumer behavior.

Using agent-based modeling to compare corrective actions for Listeria contamination in produce packinghouses.

The complex environment of a produce packinghouse can facilitate the spread of pathogens such as Listeria monocytogenes in unexpected ways. This can lead to finished product contamination and potential foodborne disease cases. There is a need for simulation-based decision support tools that can test different corrective actions and are able to account for a facility's interior cross-contamination dynamics. Thus, we developed agent-based models of Listeria contamination dynamics for two produce packinghouse facilities; agents in the models represented equipment surfaces and employees, and models were parameterized using observations, values from published literature and expert opinion. Once validated with historical data from Listeria environmental sampling, each model's baseline conditions were investigated and used to determine the effectiveness of corrective actions in reducing prevalence of agents contaminated with Listeria and concentration of Listeria on contaminated agents. Evaluated corrective actions included reducing incoming Listeria, modifying cleaning and sanitation strategies, and reducing transmission pathways, and combinations thereof. Analysis of Listeria contamination predictions revealed differences between the facilities despite their functional similarities, highlighting that one-size-fits-all approaches may not always be the most effective means for selection of corrective actions in fresh produce packinghouses. Corrective actions targeting Listeria introduced in the facility on raw materials, implementing risk-based cleaning and sanitation, and modifying equipment connectivity were shown to be most effective in reducing Listeria contamination prevalence. Overall, our results suggest that a well-designed cleaning and sanitation schedule, coupled with good manufacturing practices can be effective in controlling contamination, even if incoming Listeria spp. on raw materials cannot be reduced. The presence of water within specific areas was also shown to influence corrective action performance. Our findings support that agent-based models can serve as effective decision support tools in identifying Listeria-specific vulnerabilities within individual packinghouses and hence may help reduce risks of food contamination and potential human exposure.

In Silico Models for Design and Optimization of Science-Based Listeria Environmental Monitoring Programs in Fresh-Cut Produce Facilities.

Food facilities need time- and cost-saving methods during the development and optimization of environmental monitoring for pathogens and their surrogates. Rapid virtual experimentation through in silico modeling can alleviate the need for extensive real-world, trial-and-error style program design. Two agent-based models of fresh-cut produce facilities were developed as a way to simulate the dynamics of Listeria in the built environment by modeling the different surfaces of equipment and employees in a facility as agents. Five sampling schemes at three time points were evaluated in silico on their ability to locate the presence of Listeria contamination in a facility with sample sites for each scheme (i.e., scenario, as modeled using scenario analysis) based on the following: the facilities' current environmental monitoring program (scenario 1), Food and Drug Administration recommendations (scenario 2), random selection (scenario 3), sites exclusively from zone 3 (i.e., sites in the production room but not directly adjacent to food contact surfaces) (scenario 4), or model prediction of elevated risk of contamination (scenario 5). Variation was observed between the scenarios on how well the Listeria prevalence of the virtually collected samples reflected the true prevalence of contaminated agents in the modeled operation. The zone 3 only (scenario 4) and model-based (scenario 5) sampling scenarios consistently overestimated true prevalence across time, suggesting that those scenarios could provide a more sensitive approach for determining if Listeria is present in the operation. The random sampling scenario (scenario 3) may be more useful for operations looking for a scheme that is most likely to reflect the true prevalence. Overall, the developed models allow for rapid virtual experimentation and evaluation of sampling schemes specific to unique fresh-cut produce facilities. IMPORTANCE Programs such as environmental monitoring are used to determine the state of a given food facility with regard to the presence of environmental pathogens, such as Listeria monocytogenes, that could potentially cross-contaminate food product. However, the design of environmental monitoring programs is complex, and there are infinite ways to conduct the sampling that is required for these programs. Experimentally evaluating sampling schemes in a food facility is time-consuming, costly, and nearly impossible. Therefore, the food industry needs science-based tools to aid in developing and refining sampling plans that reduce the risk of harboring contamination. Two agent-based models of two fresh-cut produce facilities reported here demonstrate a novel way to evaluate how different sampling schemes can be rapidly evaluated across multiple time points as a way to understand how sampling can be optimized in an effort to locate the presence of Listeria in a food facility.

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.

EnABLe: An agent-based model to understand Listeria dynamics in food processing facilities.

Detection of pathogens in food processing facilities by routine environmental monitoring (EM) is essential to reduce the risk of foodborne illness but is complicated by the complexity of equipment and environment surfaces. To optimize design of EM programs, we developed EnABLe ("Environmental monitoring with an Agent-Based Model of Listeria"), a detailed and customizable agent-based simulation of a built environment. EnABLe is presented here in a model system, tracing Listeria spp. (LS) (an indicator for conditions that allow the presence of the foodborne pathogen Listeria monocytogenes) on equipment and environment surfaces in a cold-smoked salmon facility. EnABLe was parameterized by existing literature and expert elicitation and validated with historical data. Simulations revealed different contamination dynamics and risks among equipment surfaces in terms of the presence, level and persistence of LS. Grouping of surfaces by their LS contamination dynamics identified connectivity and sanitary design as predictors of contamination, indicating that these features should be considered in the design of EM programs to detect LS. The EnABLe modeling approach is particularly timely for the frozen food industry, seeking science-based recommendations for EM, and may also be relevant to other complex environments where pathogen contamination presents risks for direct or indirect human exposure.

Postharvest Supply Chain with Microbial Travelers: a Farm-to-Retail Microbial Simulation and Visualization Framework.

Fresh produce supply chains present variable and diverse conditions that are relevant to food quality and safety because they may favor microbial growth and survival following contamination. This study presents the development of a simulation and visualization framework to model microbial dynamics on fresh produce moving through postharvest supply chain processes. The postharvest supply chain with microbial travelers (PSCMT) tool provides a modular process modeling approach and graphical user interface to visualize microbial populations and evaluate practices specific to any fresh produce supply chain. The resulting modeling tool was validated with empirical data from an observed tomato supply chain from Mexico to the United States, including the packinghouse, distribution center, and supermarket locations, as an illustrative case study. Due to data limitations, a model-fitting exercise was conducted to demonstrate the calibration of model parameter ranges for microbial indicator populations, i.e., mesophilic aerobic microorganisms (quantified by aerobic plate count and here termed APC) and total coliforms (TC). Exploration and analysis of the parameter space refined appropriate parameter ranges and revealed influential parameters for supermarket indicator microorganism levels on tomatoes. Partial rank correlation coefficient analysis determined that APC levels in supermarkets were most influenced by removal due to spray water washing and microbial growth on the tomato surface at postharvest locations, while TC levels were most influenced by growth on the tomato surface at postharvest locations. Overall, this detailed mechanistic dynamic model of microbial behavior is a unique modeling tool that complements empirical data and visualizes how postharvest supply chain practices influence the fate of microbial contamination on fresh produce.IMPORTANCE Preventing the contamination of fresh produce with foodborne pathogens present in the environment during production and postharvest handling is an important food safety goal. Since studying foodborne pathogens in the environment is a complex and costly endeavor, computer simulation models can help to understand and visualize microorganism behavior resulting from supply chain activities. The postharvest supply chain with microbial travelers (PSCMT) model, presented here, provides a unique tool for postharvest supply chain simulations to evaluate microbial contamination. The tool was validated through modeling an observed tomato supply chain. Visualization of dynamic contamination levels from harvest to the supermarket and analysis of the model parameters highlighted critical points where intervention may prevent microbial levels sufficient to cause foodborne illness. The PSCMT model framework and simulation results support ongoing postharvest research and interventions to improve understanding and control of fresh produce contamination.

Design Elements of Listeria Environmental Monitoring Programs in Food Processing Facilities: A Scoping Review of Research and Guidance Materials.

Occurrence of Listeria monocytogenes (Lm), the causative agent of listeriosis, in food processing facilities presents considerable challenges to food producers and food safety authorities. Design of an effective, risk-based environmental monitoring (EM) program is essential for finding and eliminating Lm from the processing environment to prevent product contamination. A scoping review was conducted to collate and synthesize available research and guidance materials on Listeria EM in food processing facilities. An exhaustive search was performed to identify all available research, industry and regulatory documents, and search results were screened for relevance based on eligibility criteria. After screening, 198 references were subjected to an in-depth review and categorized according to objectives for conducting Listeria sampling in food processing facilities and food sector. Mapping of the literature revealed research and guidance gaps by food sector, as fresh produce was the focus in only 10 references, compared to 72 on meat, 52 on fish and seafood, and 50 on dairy. Review of reported practices and guidance highlighted key design elements of EM, including the number, location, timing and frequency of sampling, as well as methods of detection and confirmation, and record-keeping. While utilization of molecular subtyping methods is a trend that will continue to advance understanding of Listeria contamination risks, improved study design and reporting standards by researchers will be essential to assist the food industry optimize their EM design and decision-making. The comprehensive collection of documents identified and synthesized in this review aids continued efforts to minimize the risk of Lm contaminated foods.

Microbial dynamics of indicator microorganisms on fresh tomatoes in the supply chain from Mexico to the USA.

Quality and safety of fresh produce are important to public health and maintaining commerce between Mexico and USA. While preventive practices can reduce risks of contamination and are generally successful, the variable environment of the supply chain of fresh produce can be suitable for introduction or proliferation of pathogenic microorganisms. As routine surveillance of these pathogens is not practical, indicator microorganisms are used to assess the sanitary conditions of production and handling environments. An opportunity exists to use indicators on fresh produce to measure how handling and transport from field to market may affect microbial populations that contribute to their quality or safety. The objective was to quantify indicator microorganisms on tomatoes sampled along the supply chain during the harvest year, in order to observe the levels and changes of populations at different locations. Roma tomatoes (n=475) were taken from the same lots (n=28) at four locations of the postharvest supply chain over five months: at arrival to and departure from the packinghouse in México, at the distribution center in Texas, and at retail in USA. Samples were analyzed individually for four microbial populations: aerobic plate count (APC), total coliforms (TC), generic Escherichia coli, and yeasts and molds (YM). APC population differed (p<0.05) from 1.9±1.1, 1.7±1.1, 2.3±1.1 and 3.5±1.4logCFU/g at postharvest, packing, distribution center and supermarket, respectively. TC populations were <1logCFU/g at postharvest, increased at packing (0.7±1.0logCFU/g), decreased in distribution (0.4±0.8logCFU/g) and increased in supermarkets (1.4±1.5logCFU/g). Generic E. coli was not identified from coliform populations in this supply chain. YM populations remained <1logCFU/g, with the exception of 1.1±1.3logCFU/g at supermarkets and tomatoes were not visibly spoiled. The levels reported from this pilot study demonstrated the dynamics within populations as influenced by time and conditions in one supply chain during a harvest year, while the large variances in some locations indicate opportunities for improvement. Overall, packinghouse and supermarket locations were identified as crucial points to control microbial safety risks.

Current intervention strategies for the microbial safety of sprouts.

Sprouts have gained popularity worldwide due to their nutritional values and health benefits. The fact that their consumption has been associated with numerous outbreaks of foodborne illness threatens the $250 million market that this industry has established in the United States. Therefore, sprout manufacturers have utilized the U.S. Food and Drug Administration recommended application of 20,000 ppm of calcium hypochlorite solution to seeds before germination as a preventative method. Concentrations of up to 200 ppm of chlorine wash are also commonly used on sprouts. However, chlorine-based treatment achieves on average only 1- to 3-log reductions in bacteria and is associated with negative health and environmental issues. The search for alternative strategies has been widespread, involving chemical, biological, physical, and hurdle processes that can achieve up to 7-log reductions in bacteria in some cases. The compilation here of the current scientific data related to these techniques is used to compare their efficacy for ensuring the microbial safety of sprouts and their practicality for commercial producers. Of specific importance for alternative seed and sprout treatments is maintaining the industry-accepted germination rate of 95% and the sensorial attributes of the final product. This review provides an evaluation of suggested decontamination technologies for seeds and sprouts before, during, and after germination and concludes that thermal inactivation of seeds and irradiation of sprouts are the most practical stand-alone microbial safety interventions for sprout production.