Population dynamics of Listeria spp., Salmonella spp., and Escherichia coli on fresh produce: A scoping review.

Collation of the current scope of literature related to population dynamics (i.e., growth, die-off, survival) of foodborne pathogens on fresh produce can aid in informing future research directions and help stakeholders identify relevant research literature. A scoping review was conducted to gather and synthesize literature that investigates population dynamics of pathogenic and non-pathogenic Listeria spp., Salmonella spp., and Escherichia coli on whole unprocessed fresh produce (defined as produce not having undergone chopping, cutting, homogenization, irradiation, or pasteurization). Literature sources were identified using an exhaustive search of research and industry reports published prior to September 23, 2021, followed by screening for relevance based on strict, a priori eligibility criteria. A total of 277 studies that met all eligibility criteria were subjected to an in-depth qualitative review of various factors (e.g., produce commodities, study settings, inoculation methodologies) that affect population dynamics. Included studies represent investigations of population dynamics on produce before (i.e., pre-harvest; n = 143) and after (i.e., post-harvest; n = 144) harvest. Several knowledge gaps were identified, including the limited representation of (i) pre-harvest studies that investigated population dynamics of Listeria spp. on produce (n = 13, 9% of pre-harvest studies), (ii) pre-harvest studies that were carried out on non-sprouts produce types grown using hydroponic cultivation practices (n = 7, 5% of pre-harvest studies), and (iii) post-harvest studies that reported the relative humidity conditions under which experiments were carried out (n = 56, 39% of post-harvest studies). These and other knowledge gaps summarized in this scoping review represent areas of research that can be investigated in future studies.

A Validated Preharvest Sampling Simulation Shows that Sampling Plans with a Larger Number of Randomly Located Samples Perform Better than Typical Sampling Plans in Detecting Representative Point-Source and Widespread Hazards in Leafy Green Fields.

Commercial leafy greens customers often require a negative preharvest pathogen test, typically by compositing 60 produce sample grabs of 150 to 375 g total mass from lots of various acreages. This study developed a preharvest sampling Monte Carlo simulation, validated it against literature and experimental trials, and used it to suggest improvements to sampling plans. The simulation was validated by outputting six simulated ranges of positive samples that contained the experimental number of positive samples (range, 2 to 139 positives) recovered from six field trials with point source, systematic, and sporadic contamination. We then evaluated the relative performance between simple random, stratified random, or systematic sampling in a 1-acre field to detect point sources of contamination present at 0.3% to 1.7% prevalence. Randomized sampling was optimal because of lower variability in probability of acceptance. Optimized sampling was applied to detect an industry-relevant point source [3 log(CFU/g) over 0.3% of the field] and widespread contamination [-1 to -4 log(CFU/g) over the whole field] by taking 60 to 1,200 sample grabs of 3 g. More samples increased the power of detecting point source contamination, as the median probability of acceptance decreased from 85% with 60 samples to 5% with 1,200 samples. Sampling plans with larger total composite sample mass increased power to detect low-level, widespread contamination, as the median probability of acceptance with -3 log(CFU/g) contamination decreased from 85% with a 150-g total mass to 30% with a 1,200-g total mass. Therefore, preharvest sampling power increases by taking more, smaller samples with randomization, up to the constraints of total grabs and mass feasible or required for a food safety objective. IMPORTANCE This study addresses a need for improved preharvest sampling plans for pathogen detection in leafy green fields by developing and validating a preharvest sampling simulation model, avoiding the expensive task of physical sampling in many fields. Validated preharvest sampling simulations were used to develop guidance for preharvest sampling protocols. Sampling simulations predicted that sampling plans with randomization are less variable in their power to detect low-prevalence point source contamination in a 1-acre field. Collecting larger total sample masses improved the power of sampling plans in detecting widespread contamination in 1-acre fields. Hence, the power of typical sampling plans that collect 150 to 375 g per composite sample can be improved by taking more, randomized smaller samples for larger total sample mass. The improved sampling plans are subject to feasibility constraints or to meet a particular food safety objective.

Growth and survival of aerobic and Gram-negative bacteria on fresh spinach in a Chinese supply chain from harvest through distribution and refrigerated storage.

Spinach is a highly perishable product that degrades over time, including due to bacteria contaminating the product prior to packaging, yet the dynamics of bacterial spoilage and factors that affect it are not well understood. Notably, while China is the top producer of spinach globally, there is limited available microbiological data in the literature for spinach supply chains in China. The overall goal of this foundational study was to establish a baseline understanding of bacterial population dynamics on spinach from harvest to 10 days postprocessing for a Chinese supply chain that includes distribution via traditional grocery (a local physical store) and eCommerce (an online store). To this end, organic spinach samples were collected at different stages in a Chinese supply chain by following the same 3 lots, starting at point-of-harvest through processing and distribution via a local grocery store and eCommerce. After distribution, the same 3 lots were stored at 4 °C with microbiological testing performed on multiple days up to day 10 postprocessing, simulating storage at the point-of-consumer. Results showed aerobic plate counts and total Gram-negative counts did not significantly differ across stages in the supply chain from harvest through processing. However, packaged spinach from the same processing facility and lots, exhibited different patterns in bacterial levels across 0 to 10 days postprocessing, depending on whether it was distributed via the local grocery store or eCommerce. Evaluation of bacterial populations performed on a subset of the packaged spinach samples indicated Gram-negative bacteria, in particular Pseudomonas, were predominant across all days of testing (days 0, 3, and 10 postprocessing), with populations differing at the genus level by day. Overall, this study improves our understanding of the dynamics of bacterial populations on spinach and provides baseline data needed for future studies.

Weather stressors correlate with Escherichia coli and Salmonella enterica persister formation rates in the phyllosphere: a mathematical modeling study.

Enteric pathogens can enter a persister state in which they survive exposure to antibiotics and physicochemical stresses. Subpopulations of such phenotypic dormant variants have been detected in vivo and in planta in the laboratory, but their formation in the natural environment remains largely unexplored. We applied a mathematical model predicting the switch rate to persister cell in the phyllosphere to identify weather-related stressors associated with E. coli and S. enterica persister formation on plants based on their population dynamics in published field studies from the USA and Spain. Model outputs accurately depicted the bi-phasic decay of bacterial population sizes measured in the lettuce and spinach phyllosphere in these studies. Predicted E. coli persister switch rate on leaves was positively and negatively correlated with solar radiation intensity and wind velocity, respectively. Likewise, predicted S. enterica persister switch rate correlated positively with solar radiation intensity; however, a negative correlation was observed with air temperature, relative humidity, and dew point, factors involved in water deposition onto the phylloplane. These findings suggest that specific environmental factors may enrich for dormant bacterial cells on plants. Our model quantifiably links persister cell subpopulations in the plant habitat with broader physical conditions, spanning processes at different granular scales.

Small Produce Farm Environments Can Harbor Diverse Listeria monocytogenes and Listeria spp. Populations.

ABSTRACT: A comprehensive understanding of foodborne pathogen diversity in preharvest environments is necessary to effectively track pathogens on farms and identify sources of produce contamination. As such, this study aimed to characterize Listeria diversity in wildlife feces and agricultural water collected from a New York state produce farm over a growing season. Water samples were collected from a pond (n = 80) and a stream (n = 52). Fecal samples (n = 77) were opportunistically collected from areas <5 m from the water sources; all samples were collected from a <0.5-km2 area. Overall, 86 (41%) and 50 (24%) of 209 samples were positive for Listeria monocytogenes and Listeria spp. (excluding L. monocytogenes), respectively. For each positive sample, one L. monocytogenes or Listeria spp. isolate was speciated by sequencing the sigB gene, thereby allowing for additional characterization based on the sigB allelic type. The 86 L. monocytogenes and 50 Listeria spp. isolates represented 8 and 23 different allelic types, respectively. A subset of L. monocytogenes isolates (n = 44) from pond water and pond-adjacent feces (representing an ∼5,000-m2 area) were further characterized by pulsed-field gel electrophoresis (PFGE); these 44 isolates represented 22 PFGE types, which is indicative of considerable diversity at a small spatial scale. Ten PFGE types were isolated more than once, suggesting persistence or reintroduction of PFGE types in this area. Given the small spatial scale, the prevalence of L. monocytogenes and Listeria spp., as well as the considerable diversity among isolates, suggests traceback investigations may be challenging. For example, traceback of finished product or processing facility contamination with specific subtypes to preharvest sources may require collection of large sample sets and characterization of a considerable number of isolates. Our data also support the adage "absence of evidence does not equal evidence of absence" as applies to L. monocytogenes traceback efforts at the preharvest level.

Effect of Weather on the Die-Off of Escherichia coli and Attenuated Salmonella enterica Serovar Typhimurium on Preharvest Leafy Greens following Irrigation with Contaminated Water.

The Food Safety Modernization Act (FSMA) includes a time-to-harvest interval following the application of noncompliant water to preharvest produce to allow for microbial die-off. However, additional scientific evidence is needed to support this rule. This study aimed to determine the impact of weather on the die-off rate of Escherichia coli and Salmonella on spinach and lettuce under field conditions. Standardized, replicated field trials were conducted in California, New York, and Spain over 2 years. Baby spinach and lettuce were grown and inoculated with an ∼104-CFU/ml cocktail of E. coli and attenuated Salmonella Leaf samples were collected at 7 time points (0 to 96 h) following inoculation; E. coli and Salmonella were enumerated. The associations of die-off with study design factors (location, produce type, and bacteria) and weather were assessed using log-linear and biphasic segmented log-linear regression. A segmented log-linear model best fit die-off on inoculated leaves in most cases, with a greater variation in the segment 1 die-off rate across trials (-0.46 [95% confidence interval {95% CI}, -0.52, -0.41] to -6.99 [95% CI, -7.38, -6.59] log10 die-off/day) than in the segment 2 die-off rate (0.28 [95% CI, -0.20, 0.77] to -1.00 [95% CI, -1.16, -0.85] log10 die-off/day). A lower relative humidity was associated with a faster segment 1 die-off and an earlier breakpoint (the time when segment 1 die-off rate switches to the segment 2 rate). Relative humidity was also found to be associated with whether die-off would comply with FSMA's specified die-off rate of -0.5 log10 die-off/day.IMPORTANCE The log-linear die-off rate proposed by FSMA is not always appropriate, as the die-off rates of foodborne bacterial pathogens and specified agricultural water quality indicator organisms appear to commonly follow a biphasic pattern with an initial rapid decline followed by a period of tailing. While we observed substantial variation in the net culturable population levels of Salmonella and E. coli at each time point, die-off rate and FSMA compliance (i.e., at least a 2 log10 die-off over 4 days) appear to be impacted by produce type, bacteria, and weather; die-off on lettuce tended to be faster than that on spinach, die-off of E. coli tended to be faster than that of attenuated Salmonella, and die-off tended to become faster as relative humidity decreased. Thus, the use of a single die-off rate for estimating time-to-harvest intervals across different weather conditions, produce types, and bacteria should be revised.

Landscape, Water Quality, and Weather Factors Associated With an Increased Likelihood of Foodborne Pathogen Contamination of New York Streams Used to Source Water for Produce Production.

There is a need for science-based tools to (i) help manage microbial produce safety hazards associated with preharvest surface water use, and (ii) facilitate comanagement of agroecosystems for competing stakeholder aims. To develop these tools an improved understanding of foodborne pathogen ecology in freshwater systems is needed. The purpose of this study was to identify (i) sources of potential food safety hazards, and (ii) combinations of factors associated with an increased likelihood of pathogen contamination of agricultural water Sixty-eight streams were sampled between April and October 2018 (196 samples). At each sampling event separate 10-L grab samples (GS) were collected and tested for Listeria, Salmonella, and the stx and eaeA genes. A 1-L GS was also collected and used for Escherichia coli enumeration and detection of four host-associated fecal source-tracking markers (FST). Regression analysis was used to identify individual factors that were significantly associated with pathogen detection. We found that eaeA-stx codetection [Odds Ratio (OR) = 4.2; 95% Confidence Interval (CI) = 1.3, 13.4] and Salmonella isolation (OR = 1.8; CI = 0.9, 3.5) were strongly associated with detection of ruminant and human FST markers, respectively, while Listeria spp. (excluding Listeria monocytogenes) was negatively associated with log10 E. coli levels (OR = 0.50; CI = 0.26, 0.96). L. monocytogenes isolation was not associated with the detection of any fecal indicators. This observation supports the current understanding that, unlike enteric pathogens, Listeria is not fecally-associated and instead originates from other environmental sources. Separately, conditional inference trees were used to identify scenarios associated with an elevated or reduced risk of pathogen contamination. Interestingly, while the likelihood of isolating L. monocytogenes appears to be driven by complex interactions between environmental factors, the likelihood of Salmonella isolation and eaeA-stx codetection were driven by physicochemical water quality (e.g., dissolved oxygen) and temperature, respectively. Overall, these models identify environmental conditions associated with an enhanced risk of pathogen presence in agricultural water (e.g., rain events were associated with L. monocytogenes isolation from samples collected downstream of dairy farms; P = 0.002). The information presented here will enable growers to comanage their operations to mitigate the produce safety risks associated with preharvest surface water use.

Predictive Models May Complement or Provide an Alternative to Existing Strategies for Assessing the Enteric Pathogen Contamination Status of Northeastern Streams Used to Provide Water for Produce Production.

While the Food Safety Modernization Act established standards for the use of surface water for produce production, water quality is known to vary over space and time. Targeted approaches for identifying hazards in water that account for this variation may improve growers' ability to address pre-harvest food safety risks. Models that utilize publicly-available data (e.g., land-use, real-time weather) may be useful for developing these approaches. The objective of this study was to use pre-existing datasets collected in 2017 (N = 181 samples) and 2018 (N = 191 samples) to train and test models that predict the likelihood of detecting Salmonella and pathogenic E. coli markers (eaeA, stx) in agricultural water. Four types of features were used to train the models: microbial, physicochemical, spatial and weather. "Full models" were built using all four features types, while "nested models" were built using between one and three types. Twenty learners were used to develop separate full models for each pathogen. Separately, to assess information gain associated with using different feature types, six learners were randomly selected and used to develop nine, nested models each. Performance measures for each model were then calculated and compared against baseline models where E. coli concentration was the sole covariate. In the methods, we outline the advantages and disadvantages of each learner. Overall, full models built using ensemble (e.g., Node Harvest) and "black-box" (e.g., SVMs) learners out-performed full models built using more interpretable learners (e.g., tree- and rule-based learners) for both outcomes. However, nested eaeA-stx models built using interpretable learners and microbial data performed almost as well as these full models. While none of the nested Salmonella models performed as well as the full models, nested models built using spatial data consistently out-performed models that excluded spatial data. These findings demonstrate that machine learning approaches can be used to predict when and where pathogens are likely to be present in agricultural water. This study serves as a proof-of-concept that can be built upon once larger datasets become available and provides guidance on the learner-data combinations that should be the foci of future efforts (e.g., tree-based microbial models for pathogenic E. coli).