Cecil Barnett-Neefs | Page 2 | Stasiewicz Food Safety Laboratory | Vision: A world where everyone, everywhere is food secure

Milk spoilage model predicts that share tables would not meaningfully increase spoilage, and improved storage systems can reduce spoilage

Experimental setups of data collection for milk internal temperature under different storage systems and refrigeration condition and external temperature. Both external and internal milk temperature and experimental setup were measured for an hour in duplicate (2 probes) over 4 d resulting in a total of 4 replicates.

Abstract

School share tables offer opportunities for food recovery and increased access to healthy foods by allowing students to donate or consume unopened items, such as cartons of milk. However, stakeholders have concerns about temperature abuse potentially causing premature milk spoilage. Although previous research showed short ambient temperature abuse of milk (under conditions representing share tables) does not meaningfully affect microbial milk quality, differences across school cafeterias (e.g., ambient temperatures, storage systems, bell schedules, refrigeration temperature) may limit the generalizability of this conclusion. To address this, the overnight refrigeration temperature and the milk’s initial contamination were predicted to be the main drivers for milk spoilage. Share tables were predicted to only cause inconsequential microbial quality changes (4 spoiled milk per million served, which would be ≤2 milk cartons spoiled per school year) under short and medium bell schedules (≤125 min of total service), even without temperature control during the lunch period. Under long (221 min) and very long (266 min) bell schedules, share tables with ambient temperature storage were predicted to have higher milk spoilage (19 and 42 spoiled milk cartons per million served, respectively), and adding storage systems was predicted to reduce the decline in milk quality (12 and 24 spoiled milk cartons per million served, respectively). These results provide a resource to support science-based decision making for the inclusion of milk in school cafeteria share tables, ultimately working to reduce food waste and address food insecurity.

DOI

https://doi.org/10.3168/jds.2024-25189

Risk Assessment Predicts Most of the Salmonellosis Risk in Raw Chicken Parts is Concentrated in Those Few Products with High Levels of High-Virulence Serotypes of Salmonella

Summary of collected USDA-FSIS publicly available datasets used in this project to characterize Salmonella contamination in chicken parts by presence, level, and serotype

Highlights

Chicken parts are rarely contaminated with high Salmonella levels, above 1 CFU/g.
High-virulence serotypes are frequently found in US chicken parts.
Most salmonellosis risk is concentrated in chicken part products above 1 CFU/g.
Most salmonellosis risk is in products with high levels of high-virulence serotypes.

Abstract

Salmonella prevalence declined in U.S. raw poultry products since adopting prevalence-based Salmonella performance standards, but human illnesses did not reduce proportionally. We used Quantitative Microbial Risk Assessment (QMRA) to evaluate public health risks of raw chicken parts contaminated with different levels of all Salmonella and specific high- and low-virulence serotypes. Lognormal Salmonella level distributions were fitted to 2012 USDA-FSIS Baseline parts survey and 2023 USDA-FSIS HACCP verification sampling data. Three different Dose-Response (DR) approaches included (i) a single DR for all serotypes, (ii) DR that reduces Salmonella Kentucky ST152 virulence, and (iii) multiple serotype-specific DR models. All scenarios found risk concentrated in the few products with high Salmonella levels. Using a single DR model with Baseline data (μ = -3.19, σ = 1.29 Log CFU/g), 68% and 37% of illnesses were attributed to the 0.7% and 0.06% of products with >1 and >10 CFU/g Salmonella, respectively. Using distributions from 2023 HACCP data (μ = -5.53, σ = 2.45), 99.8% and 99.0% of illnesses were attributed to the 1.3% and 0.4% of products with >1 and >10 CFU/g Salmonella, respectively. Scenarios with serotype-specific DR models showed more concentrated risk at higher levels. Baseline data showed 92% and 67% and HACCP data showed >99.99% and 99.96% of illnesses attributed to products with >1 and >10 CFU/g Salmonella, respectively. Regarding serotypes using Baseline or HACCP input data, 0.002% and 0.1% of illnesses were attributed to the 0.2% and 0.4% of products with >1 CFU/g of Kentucky ST152, respectively, while 69% and 83% of illnesses were attributed to the 0.3% and 0.6% of products with >1 CFU/g of Enteritidis, Infantis, or Typhimurium, respectively. Therefore, public health risk in chicken parts is concentrated in finished products with high levels and specifically high levels of high-virulence serotypes. Low-virulence serotypes like Kentucky contribute few human cases.

DOI

https://doi.org/10.1016/j.jfp.2024.100304

Time and temperature abuse of milk in conditions representing a school cafeteria share table does not meaningfully reduce microbial quality

Abstract

Share tables (ST) are tables or stations in school cafeterias where students can return unopened foods and beverages, providing an opportunity to access these items at no cost. Currently, research suggests that milk is among the most wasted items in breakfast and lunch programs in the United States. Share tables present a simple solution for reducing milk waste, but research is needed to understand the microbial spoilage potential of milk in ST. To this end, uninoculated milk cartons and milk cartons inoculated with 2 to 3 log₁₀(cfu/mL) Pseudomonas poae, a fast-growing psychrotroph, was exposed to ambient temperature during winter (mean temperature = 20.3°C) and summer (23.1°C) for 125 min, repeated over 5 d (the length of a school week). Microbial counts in the inoculated milk cartons increased linearly, exceeding the spoilage threshold of 6.0 log₁₀(cfu/mL) after d 3 and after d 4 in the winter and summer season trials, respectively. In the winter trial, the microbial counts for uninoculated milk cartons never exceeded the lower limit of detection, 2.31 log₁₀(cfu/mL), and in the summer trials, microbial counts never reached the spoilage threshold, indicating that initial contamination is a driving factor of microbial milk spoilage. Regardless of sharing status or seasonality, the greatest changes in counts for inoculated milk cartons occurred during overnight refrigeration, ranging from 0.56 to 1.4 log₁₀(cfu/mL), while during the share table ranged from no observable change up to 0.29 log₁₀(cfu/mL), emphasizing that school nutrition personnel should focus efforts on tightly controlling refrigeration temperatures and returning milk to refrigeration as soon as possible. A previously developed model for school cafeteria share tables was adapted to understand the typical residence time of milk in a simulated cafeteria with an ambient temperature share table for the summer and winter seasons over 1,000 wk. Milk was predicted to have a very short mean residence time (85 min) regardless of sharing status or season, with 99.8% of milk consumed, discarded, or donated within the first 2 d. As a result, only 3 out of 451,410 and 6 out of 451,410 simulated milks spoiled in the winter and summer seasons, respectively. The data generated here can be used to inform science-based decision-making for including milk in share tables, or applied to any system where one might have to accept short-term unrefrigerated storage of milk to meet a waste reduction or food security goal.

DOI

https://doi.org/10.3168/jds.2023-24175

Modeling Preharvest Cyclospora cayetanensis Sampling and Testing for Various Water and Produce Sampling Plans

Scenario Analysis Framework, a 45-day growth cycle was simulated. Water and produce testing occurred as described in Table 3. Two contamination level scenarios (high and low level) were simulated. Two contamination frequencies (daily or once per season) were simulated. This resulted in a total of 48 combinations.

Highlights

Assay performance was modeled for detecting contamination during cilantro production.
Frequent water testing most powerfully detects water contamination.
End of season produce testing most powerfully detects in-field contamination.

Abstract

As of August 2023, the two U.S. Food and Drug Administration (FDA) official detection methods for C. cayetanensis are outlined in the FDA Bacteriological Analytical Manual (BAM) Chapters 19b (produce testing) and 19c (agricultural water testing). These newly developed detection methods have been shown to not always detect contamination when present at low levels. Yet, industry and regulators may choose to use these methods as part of their monitoring and verification activities while detection methods continue to be improved. This study uses simulation to better understand the performance of these methods for various produce and water sampling plans. To do so, we used published FDA test validation data to fit a logistic regression model that predicts the methods’ detection rate given the number of oocysts present in a 10-L agricultural water or 25 g produce sample. By doing so, we were able to determine contamination thresholds at which different numbers of samples (n = 1, 2, 4, 8, 16, and 32) would be adequate for detecting contamination. Furthermore, to evaluate sampling plans in use cases, a simulation was developed to represent C. cayetanensis contamination in agricultural water and on cilantro throughout a 45-day growth cycle. The model included uncertainty around the contamination sources, including scenarios of unintentionally contaminated irrigation water or in-field contamination. The results demonstrate that in cases where irrigation water was the contamination source, frequent water testing proved to be more powerful than produce testing. In scenarios where contamination occurred in-field, conducting frequent produce testing or testing produce toward the end of the season more reliably detected contamination. This study models the power of C. cayetanensis detection methods to understand the sampling plan performance and how these methods can be better used to monitor this emerging food safety hazard.

DOI

https://doi.org/10.1016/j.jfp.2023.100161

Multi-Spectral Sorting Based on Visibly High-Risk Kernels Sourced from Another Country Reduces Fumonisin and Toxigenic Fusarium on Maize Kernels

Highlights

Fumonisin levels in Kenyan maize were significantly reduced after spectral sorting calibrated to high-risk kernels from Ghana
Spectral sorting reduced toxigenic Fusarium populations in maize kernels
Sorting based on visibly high-risk kernels represents a potentially generalizable approach to physical remediation of fumonisin

Abstract

Fusarium species infect maize crops leading to production of fumonisin by their toxigenic members. Elimination of microbes is critical in mitigating further post-harvest spoilage and toxin accumulation. The current study investigates the efficacy of a previously described multi-spectral sorting technique to analyze the reduction of fumonisin and toxigenic Fusarium species found contaminating maize kernels in Kenya. Maize samples (n = 99) were collected from six mycotoxin hotspot counties in Kenya (Embu, Meru, Tharaka Nithi, Machakos, Makueni and Kitui County) and analyzed for aflatoxin and fumonisin using commercial ELISA kits. Aflatoxin levels in majority (91%) of the samples were below the 10 ng/g threshold set by the Kenya Bureau of Standards and therefore not studied further. The 23/99 samples that had > 2,000 ng/g of fumonisin were selected for sorting. The sorter was calibrated using kernels sourced from Ghana to reject visibly high-risk kernels for fumonisin contamination using reflectance at nine distinct wavelengths (470-1,550 nm). Accepted and rejected streams were tested for fumonisin using ELISA, and the presence of toxigenic Fusarium using qPCR. After sorting, there was a significant (p<0.001) reduction of fumonisin, by an average of 1.8 log ng/g (98%) and ranging between 0.14 to 2.7 log ng/g reduction (28-99.8%) with a median mass rejection rate of 1.9% (ranged 0% to 48%). The fumonisin rejection rate ranged between 0 to 99.8% with a median of 77%. There was also a significant reduction (p=0.005) in the proportion of DNA represented by toxigenic Fusarium, from a mean of 30 to 1.4%. This study demonstrates the use of multi-spectral sorting as a potential post-harvest intervention tool for the reduction of Fusarium species and pre-formed fumonisin. The spectral sorting approach of this study suggests that classification algorithms based on high-risk visual features associated with mycotoxin can be applied across different sources of maize to reduce fumonisin.

DOI

https://doi.org/10.1016/j.jfp.2023.100142

Simulation Evaluation of Power of Sampling Plans to Detect Cronobacter in Powdered Infant Formula Production

Highlights

Sampling by Codex guidelines detects Cronobacter in a recalled PIF batch profile.
Sampling would not reliably detect Cronobacter in a non-recalled batch profile.
Sampling PIF with stratification is potentially more powerful than random sampling.
Taking more samples, even if smaller, increases the power to detect contamination.

Abstract

Cronobacter is a hazard in Powdered Infant Formula (PIF) products that is hard to detect due to localized and low-level contamination. We adapted a previously published sampling simulation to PIF sampling and benchmarked industry-relevant sampling plans across different numbers of grabs, total sample mass, and sampling patterns. We evaluated performance to detect published Cronobacter contamination profiles for a recalled PIF batch [42% prevalence, −1.8 ± 0.7 log(CFU/g)] and a reference, nonrecalled, PIF batch [1% prevalence, −2.4 ± 0.8 log(CFU/g)]. Simulating a range of numbers of grabs [n = 1–22,000 (representing testing every finished package)] with 300 g total composite mass showed that taking 30 or more grabs detected contamination reliably (<1% median probability to accept the recalled batch). Benchmarking representative sampling plans ([n = 30, mass grab = 10g], [n = 30, m = 25g], [n = 60, m = 25g], [n = 180, m = 25g]) showed that all plans would reject the recalled batch (<1% median probability to accept) but would rarely reject the reference batch (>50% median probability of acceptance, all plans). Overall, (i) systematic or stratified random sampling patterns are equal to or more powerful than random sampling of the same sample size and total sampled mass, and, (ii) taking more samples, even if smaller, can increase the power to detect contamination.

DOI

https://doi.org/10.1016/j.jfp.2023.100115

Leafy Green Farm-to-Customer Process Model Predicts Product Testing Is Most Effective at Detecting Contamination When Conducted Early in the System before Effective Interventions

The scope of the scenarios included. Three contamination patterns, seven systems, and seven sampling plans. Each was evaluated individually to predict the sampling plan power and the effect on total adulterant cells (TACs) that could reach the system endpoint.

Abstract

Commercial leafy green supply chains often are required to have test and reject (sampling) plans for specific microbial adulterants at primary production or finished product packing for market access. To better understand the impact of this type of sampling, this study simulated the effect of sampling (from preharvest to consumer) and processing interventions (such as produce wash with antimicrobial chemistry) on the microbial adulterant load reaching the system endpoint (customer). This study simulated seven leafy green systems, an optimal system (all interventions), a suboptimal system (no interventions), and five systems where single interventions were removed to represent single process failures, resulting in 147 total scenarios. The all-interventions scenario resulted in a 3.4 log reduction (95% confidence interval [CI], 3.3 to 3.6) of the total adulterant cells that reached the system endpoint (endpoint TACs). The most effective single interventions were washing, prewashing, and preharvest holding, 1.3 (95% CI, 1.2 to 1.5), 1.3 (95% CI, 1.2 to 1.4), and 0.80 (95% CI, 0.73 to 0.90) log reduction to endpoint TACs, respectively. The factor sensitivity analysis suggests that sampling plans that happen before effective processing interventions (preharvest, harvest, and receiving) were most effective at reducing endpoint TACs, ranging between 0.05 and 0.66 log additional reduction compared to systems with no sampling. In contrast, sampling postprocessing (finished product) did not provide meaningful additional reductions to the endpoint TACs (0 to 0.04 log reduction). The model suggests that sampling used to detect contamination was most effective earlier in the system before effective interventions. Effective interventions reduce undetected contamination levels and prevalence, reducing a sampling plan’s ability to detect contamination.

DOI

https://doi.org/10.1128/aem.00347-23

Perspective: Challenges with product testing in powdered infant formula

Abstract

Right now, many concerned parents are probably thinking, how could Cronobacter sakazakii get through the powdered infant formula (PIF) production system? Shouldn’t a food safety monitoring system find such a serious problem? Aren’t those products tested before arriving on shelves? And the very challenging answer is yes, these products were likely tested with a standardized sampling and testing method consistent with regulatory guidance. In fact, in an official statement, the company said all finished products are tested for Salmonella spp. and Cronobacter sakazakii before they are released, and the product samples the company retained tested negative for the implicated microorganism related to the complaints (Abbott, 2022). This begs the questions, how powerful can the current sampling and testing program be, and is there an end-product testing strategy that would guarantee that this would not happen again? The short answers are that current sampling and testing are not that powerful, and no, testing will not ever guarantee complete safety.

DOI

https://doi.org/10.3168/jds.2022-22374