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.

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. HIGHLIGHTS

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.

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.

No abstract