Tracking an Escherichia coli O157:H7-contaminated batch of leafy greens through a pilot-scale fresh-cut processing line

Quantitative risk assessment-epidemic curve prediction model for leafy green outbreak investigation

The objective of this study was to leverage quantitative risk assessment to investigate possible root cause(s) of foodborne illness outbreaks related to Shiga toxin-producing Escherichia coli O157:H7 (STEC O157) infections in leafy greens in the United States. To this end, we developed the FDA leafy green quantitative risk assessment epidemic curve prediction model (FDA-LG QRA-EC) that simulated the lettuce supply chain. The model was used to predict the number of reported illnesses and the epidemic curve associated with lettuce contaminated with STEC O157 for a wide range of scenarios representing various contamination conditions and facility processing/sanitation practices. Model predictions were generated for fresh-cut and whole lettuce, quantifying the differing impacts of facility processing and home preparation on predicted illnesses. Our model revealed that the timespan (i.e., number of days with at least one reported illness) and the peak (i.e., day with the most predicted number of reported illnesses) of the epidemic curve of a STEC O157-lettuce outbreak were not strongly influenced by facility processing/sanitation practices and were indications of contamination pattern among incoming lettuce batches received by the facility or distribution center. Through comparisons with observed number of illnesses from recent STEC O157-lettuce outbreaks, the model identified contamination conditions on incoming lettuce heads that could result in an outbreak of similar size, which can be used to narrow down potential root cause hypotheses.

Multistate Outbreaks of Foodborne Illness in the United States Associated With Fresh Produce From 2010 to 2017

In the United States, the consumption of fresh fruits and vegetables has increased during recent years as consumers seek to make healthier lifestyle choices. However, the number of outbreaks associated with fresh produce that involve cases in more than one state (multistate) has increased concomitantly. As the distance along the farm-to-fork continuum has lengthened over time, there are also more opportunities for fresh produce contamination with bacterial pathogens before it reaches the consumer. This review provides an overview of the three bacterial pathogens (i.e., pathogenic Escherichia coli, Listeria monocytogenes, and Salmonella enterica) associated with multistate fresh produce outbreaks that occurred between 2010 and 2017 in the U.S. Possible routes of fresh produce contamination, including pre- and post-harvest, are summarized and outcomes of selected outbreaks within this timeframe are highlighted. Eighty-five multistate outbreaks linked to fresh produce with a confirmed etiology occurred from 2010 to 2017. Cross-contamination within the distribution chain and poor agricultural practices, along with the production of sprouts and importation of fresh produce were frequently implicated contributors to these events. The evolution of the food supply chain in the U.S. necessitates an examination of multistate outbreaks to shed light on factors that increase the scale of these events.

Modified Coring Tool Designs Reduce Iceberg Lettuce Cross-Contamination

Contaminated coring tools may transfer bacteria to iceberg lettuce. The efficiency of coring tool design modifications in reducing bacterial transfer to lettuce heads was evaluated under simulated field operations. The standard coring tool consists of a stainless steel cylindrical tube welded to a tab that is inserted into a plastic handle. Design modifications included removal of the welded portion, incorporation of a shorter front straight bottom edge, or an angled bottom edge toward the front. In the first study, coring tools of four different designs were inoculated by dipping in a tryptic soy broth (TSB) suspension that contained 8.85 Log CFU/mL of Escherichia coli K-12 and then were used to core 100 lettuce heads, consecutively. Use of the standard tool resulted in 91% ± 9% positive lettuce heads. Removing the welded surface from the standard tool resulted in the highest reduction of E. coli transfer (44% ± 11.9% positive lettuce heads, P < 0.05), whereas incorporation of a short front straight edge with no welding resulted in 65.6% ± 5.6% of the cored lettuce heads being positive for E. coli. Removal of the welded surface resulted in a 40% decrease in E. coli contamination among the last 20 cored lettuce heads (81 to 100), which indicates that coring tool design modifications resulted in reduced cross-contamination. In the second study, the transfer of Salmonella to coring tools after their immersion in rinsing solutions was evaluated using imaging. The tools were dip inoculated for 2 min in water, water with lettuce extract, or TSB containing 7 Log CFU/mL bioluminescent Salmonella Newport; they were then imaged to observe spatial distribution of bacteria. There was greater retention and spatial distribution of Salmonella on the surface of tools immersed in water containing lettuce extract than in TSB and water. The results of the second study indicate that rinsing solutions that contain lettuce particulate and organic load could facilitate cross-contamination of Salmonella Newport to tool surfaces.

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.

A Mathematical Model for Pathogen Cross-Contamination Dynamics during the Postharvest Processing of Leafy Greens

We developed a probabilistic mathematical model for the postharvest processing of leafy greens focusing on Escherichia coli O157:H7 contamination of fresh-cut romaine lettuce as the case study. Our model can (i) support the investigation of cross-contamination scenarios, and (ii) evaluate and compare different risk mitigation options. We used an agent-based modeling framework to predict the pathogen prevalence and levels in bags of fresh-cut lettuce and quantify spread of E. coli O157:H7 from contaminated lettuce to surface areas of processing equipment. Using an unbalanced factorial design, we were able to propagate combinations of random values assigned to model inputs through different processing steps and ranked statistically significant inputs with respect to their impacts on selected model outputs. Results indicated that whether contamination originated on incoming lettuce heads or on the surface areas of processing equipment, pathogen prevalence among bags of fresh-cut lettuce and batches was most significantly impacted by the level of free chlorine in the flume tank and frequency of replacing the wash water inside the tank. Pathogen levels in bags of fresh-cut lettuce were most significantly influenced by the initial levels of contamination on incoming lettuce heads or surface areas of processing equipment. The influence of surface contamination on pathogen prevalence or levels in fresh-cut bags depended on the location of that surface relative to the flume tank. This study demonstrates that developing a flexible yet mathematically rigorous modeling tool, a "virtual laboratory," can provide valuable insights into the effectiveness of individual and combined risk mitigation options.

Transfer and Redistribution of Salmonella Typhimurium LT2 and Escherichia coli O157:H7 during Pilot-Scale Processing of Baby Spinach, Cilantro, and Romaine Lettuce

Several outbreaks of foodborne illness traced to leafy greens and culinary herbs have been hypothesized to involve cross-contamination during washing and processing. This study aimed to assess the redistribution of Salmonella Typhimurium LT2 during pilot-scale production of baby spinach and cilantro and redistribution of Escherichia coli O157:H7 during pilot-scale production of romaine lettuce. Four inoculated surrogate:uninoculated product weight ratios (10:100, 5:100, 1:100, and 0.5:100) and three inoculation levels (103, 101, and 10-1 CFU/g) were used for the three commodities. For each of three trials per condition, 5-kg batches containing uninoculated product and spot-inoculated surrogate products at each ratio and inoculation level were washed for 90 s in a 3.6-m-long flume tank through which 890 L of sanitizer-free, filtered tap water was circulated. After washing and removing the inoculated surrogate products, washed product (∼23, 225-g samples per trial) was analyzed for presence or absence of Salmonella Typhimurium or E. coli O157:H7 by using the GeneQuence Assay. For baby spinach, cilantro, and romaine lettuce, no significant differences ( P > 0.05) in the percentage of positive samples were observed at the same inoculation level and inoculated:uninoculated weight ratio. For each pathogen product evaluated (triplicate trials), inoculation level had a significant impact on the percentage of positive samples after processing, with the percentage of positive samples decreasing, as the initial surrogate inoculation level decreased. The weight ratio of contaminated:noncontaminated product plays an important role: positive samples ranged from 0% to 11.6% ± 2.05% and from 68.1% ± 33.6% to 100% among the four ratios at inoculation of 10-1 and 101 CFU/g, respectively. To our knowledge, this study is the first to assess the redistribution of low levels of pathogens from incoming product to leafy greens during processing and should provide important data for microbial risk assessments and other types of food safety analyses related to fresh-cut leafy greens.

Effect of Water Hardness on Efficacy of Sodium Hypochlorite Inactivation of Escherichia coli O157:H7 in Water

This study examined how the hardness of water affected the efficacy of sodium hypochlorite in inactivating Escherichia coli O157:H7 in water. Water was prepared at different degrees of total hardness (0, 50, 100, 200, 500, 1,000, 2,000, and 5,000 mg/liter CaCO₃). Inactivation was assessed at different levels of free chlorine (0, 0.2, 0.5, and 1.0 ppm) at 2 to 4°C and pH 6.5. Thirty milliliters of chlorinated water was inoculated with 6 log CFU/ml of E. coli O157:H7 and allowed to mix for 3, 10, 20, or 30 s. In the absence of sodium hypochlorite, no reduction in counts of E. coli O157:H7 was observed regardless of the degree of water hardness. However, in the presence of hard water, under certain chlorine concentrations and exposure times, the reduction of E. coli O157:H7 in chlorinated hard water was significantly less than the reduction observed in chlorinated deionized water. For example, after exposure to 0.5 ppm of free chlorine for 10 s, E. coli O157:H7 counts were reduced by 4.8 ± 1.4, 2.0 ± 1.3, 1.6 ± 0.7, 0.5 ± 0.7, and 0.0 ± 0.1 log CFU/ml in water containing 0, 100, 1,000, 2,000, and 5,000 mg/liter CaCO₃, respectively. With the exception of 5,000 mg/liter CaCO₃, the effect of water hardness was no longer visible after 20 s of exposure to 0.5 ppm of free chlorine. Also, hard water significantly lowered the efficacy of sodium hypochlorite at 3 s of exposure to 1.0 ppm of free chlorine. But after 20 s of exposure to 1.0 ppm of free chlorine, the impact of water hardness was no longer observed. This study demonstrated that water hardness can affect the germicidal efficacy of sodium hypochlorite, and such an impact may or may not be apparent depending on the condition of the solution and the treatment time at which the observation is made. Under the conditions typically seen in commercial produce washing operations, the impact of water hardness on chlorine efficacy is likely to be insignificant compared with that of organic load.

Guidelines To Validate Control of Cross-Contamination during Washing of Fresh-Cut Leafy Vegetables

The U.S. Food and Drug Administration requires food processors to implement and validate processes that will result in significantly minimizing or preventing the occurrence of hazards that are reasonably foreseeable in food production. During production of fresh-cut leafy vegetables, microbial contamination that may be present on the product can spread throughout the production batch when the product is washed, thus increasing the risk of illnesses. The use of antimicrobials in the wash water is a critical step in preventing such water-mediated cross-contamination; however, many factors can affect antimicrobial efficacy in the production of fresh-cut leafy vegetables, and the procedures for validating this key preventive control have not been articulated. Producers may consider three options for validating antimicrobial washing as a preventive control for cross-contamination. Option 1 involves the use of a surrogate for the microbial hazard and the demonstration that cross-contamination is prevented by the antimicrobial wash. Option 2 involves the use of antimicrobial sensors and the demonstration that a critical antimicrobial level is maintained during worst-case operating conditions. Option 3 validates the placement of the sensors in the processing equipment with the demonstration that a critical antimicrobial level is maintained at all locations, regardless of operating conditions. These validation options developed for fresh-cut leafy vegetables may serve as examples for validating processes that prevent cross-contamination during washing of other fresh produce commodities.

Prevalence and Characteristics of Salmonella Serotypes Isolated from Fresh Produce Marketed in the United States

Salmonella continues to rank as one of the most costly foodborne pathogens, and more illnesses are now associated with the consumption of fresh produce. The U.S. Department of Agriculture Microbiological Data Program (MDP) sampled select commodities of fresh fruit and vegetables and tested them for Salmonella, pathogenic Escherichia coli, and Listeria. The Salmonella strains isolated were further characterized by serotype, antimicrobial resistance, and pulsed-field gel electrophoresis profile. This article summarizes the Salmonella data collected by the MDP between 2002 and 2012. The results show that the rates of Salmonella prevalence ranged from absent to 0.34% in cilantro. A total of 152 isolates consisting of over 50 different serotypes were isolated from the various produce types, and the top five were Salmonella enterica serotype Cubana, S. enterica subspecies arizonae (subsp. IIIa) and diarizonae (subsp. IIIb), and S. enterica serotypes Newport, Javiana, and Infantis. Among these, Salmonella serotypes Newport and Javiana are also listed among the top five Salmonella serotypes that caused most foodborne outbreaks. Other serotypes that are frequent causes of infection, such as S. enterica serotypes Typhimurium and Enteritidis, were also found in fresh produce but were not prevalent. About 25% of the MDP samples were imported produce, including 65% of green onions, 44% of tomatoes, 42% of hot peppers, and 41% of cantaloupes. However, imported produce did not show higher numbers of Salmonella-positive samples, and in some products, like cilantro, all of the Salmonella isolates were from domestic samples. About 6.5% of the Salmonella isolates were resistant to the antimicrobial compounds tested, but no single commodity or serotype was found to be the most common carrier of resistant strains or of resistance. The pulsed-field gel electrophoresis profiles of the produce isolates showed similarities with Salmonella isolates from meat samples and from outbreaks, but there were also profile diversities among the strains within some serotypes, like Salmonella Newport.