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.

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.