Evaluation of the Impact of Skewness, Clustering, and Probe Sampling Plan on Aflatoxin Detection in Corn

Abstract

Probe sampling plans for aflatoxin in corn attempt to reliably estimate concentrations in bulk corn given complications like skewed contamination distribution and hotspots. To evaluate and improve sampling plans, three sampling strategies (simple random sampling, stratified random sampling, systematic sampling with U.S. GIPSA sampling schemes), three numbers of probes (5, 10, 100, the last a proxy for autosampling), four clustering levels (1, 10, 100, 1,000 kernels/cluster source), and six aflatoxin concentrations (5, 10, 20, 40, 80, 100 ppb) were assessed by Monte‐Carlo simulation. Aflatoxin distribution was approximated by PERT and Gamma distributions of experimental aflatoxin data for uncontaminated and naturally contaminated single kernels. The model was validated against published data repeatedly sampling 18 grain lots contaminated with 5.8–680 ppb aflatoxin. All empirical acceptance probabilities fell within the range of simulated acceptance probabilities. Sensitivity analysis with partial rank correlation coefficients found acceptance probability more sensitive to aflatoxin concentration (−0.87) and clustering level (0.28) than number of probes (−0.09) and sampling strategy (0.04). Comparison of operating characteristic curves indicate all sampling strategies have similar average performance at the 20 ppb threshold (0.8–3.5% absolute marginal change), but systematic sampling has larger variability at clustering levels above 100. Taking extra probes improves detection (1.8% increase in absolute marginal change) when aflatoxin is spatially clustered at 1,000 kernels/cluster, but not when contaminated grains are homogenously distributed. Therefore, taking many small samples, for example, autosampling, may increase sampling plan reliability. The simulation is provided as an R Shiny web app for stakeholder use evaluating grain sampling plans.

DOI: https://doi.org/10.1111/risa.13721

Eleanore Hansen

Elea was an undergraduate research assistant in the lab during her junior and senior years in the food science program. She performed DNA extraction, PCR, and gel electrophoresis to support AJ Taylor’s work in detecting Listeria monocytogenes persistence. Elea also helped Shannon Rezac test combinations of antimicrobials against L. monocytogenes in a miniature ham model.

After completing her BS in Food Science and Chemistry at UIUC, Elea went to the University of Minnesota for a master’s program in Food Science with an emphasis on food safety and microbiology. She is currently working to isolate novel bacteriophage from wastewater to combat Salmonella in foods. In Fall 2021, Elea is planning to begin a PhD program at the University of Minnesota in Regulatory Toxicology.

Elea enjoys eating (a lot), listening to true crime stories, yarn crafting, and reading about food safety scandals.”

Yawei Lin

Yawei worked as an undergraduate research assistant in Dr. Stasiewicz’s lab in Spring 2019. She helped with the literature review to collect useful information for developing QMRA for share tables in the school cafeteria. She also helped with mycotoxin detection in wheat kernels.

She is now a M.S student in food safety at Michigan State University, working on microbial assessment during wheat tempering and determine the effectiveness of heat pasteurization during processing.

A.J. Taylor

A.J’s project was focused on understanding two possible facets for detecting persistence in the foodborne pathogen, Listeria monocytogenes. He looked at detection methods through either using bioinformatic tools or by growth responses to various biochemical energy sources.  While he was unable to find conclusive detection methods, it narrowed down methods for future researchers in detecting persistent L. monocytogenes.

After completing his Master’s in Food Science with Dr. Stasiewicz,  he moved onto a Ph.D. program in Food Science with Dr. Nicki Engeseth at the University of Illinois at Urbana-Champaign and now he is working on understanding the parameters that influence cacao bean fermentation to make chocolate.

Enabling cost-effective screening for antimicrobials against Listeria monocytogenes in ham

Abstract

Ready-to-eat (RTE) meat products, such as deli ham, can support the growth of Listeria monocytogenes (LM) which can cause severe illness in immunocompromised individuals. The objectives of this study were to validate a miniature ham model (MHM) against the ham slice method and screen antimicrobial combinations to control LM on ham using response surface methodology (RSM) as a time- and cost-effective high-throughput screening tool. The effect of nisin (Ni), potassium lactate sodium acetate (PLSDA), lauric arginate (LAG), lytic bacteriophage (P100), and Ɛ-polylysine (EPL) added alone, or in combination, was determined on the MHM over 12 days of storage. Results showed the MHM accurately mimics the ham slice method since no statistical differences were found (p=0.526) in the change of LM cell counts in MHM and slice counts after 12 days of storage at 4°C for treated and untreated hams. The MHM was then used to screen antimicrobial combinations using an on-face design and three center points in a central composite design. The RSM was tested using a cocktail of five LM strains isolated from foodborne disease outbreaks. Three levels of the above mentioned antimicrobials were used in combination for a total of 28 runs performed in triplicate. The change of LM cell counts were determined after 12 days of storage at 4°C. All tested antimicrobials were effective on reducing LM cell counts on ham when added alone. A significant antagonistic interaction (p=0.002) was identified by the RSM between LAG and P100, where this antimicrobial combination caused a 2.2 logCFU/g change of LM cell counts after 12 days of storage. Two interactions, between Ni and EPL (p=0.058), and Ni and P100 (p=0.068), showed possible synergistic effects against LM on the MHM. Other interactions were clearly non-significant, suggesting additive effects. In future work, the developed MHM in combination with RSM can be used as a high-throughput method to analyze novel antimicrobial treatments against LM.

DOI: https://doi.org/10.4315/JFP-20-435

Literature Review Investigating Intersections between US Foodservice Food Recovery and Safety

Abstract

Food waste is increasingly scrutinized due to the projected need to feed nine billion people in 2050. Food waste squanders many natural resources and occurs at all stages of the food supply chain, but economic and environmental costs are highest at later stages due to value and resource addition throughout the supply chain. Food recovery is the practice of preventing surplus food from being landfill disposed. It provides new opportunities to utilize food otherwise wasted, such as providing it to food insecure populations. Previous research suggests that consumer willingness to waste is higher if there is a perceived food safety risk. Yet, segments of the population act in contrast to conservative food safety risk management advice when food is free or extremely discounted. Therefore, food recovery and food safety may be competing priorities. This narrative review identifies the technical, regulatory, and social context relationships between food recovery and food safety, with a focus on US foodservice settings. The review identifies the additional steps in the foodservice process that stem from food recovery – increased potential for cross-contamination and hazard amplification due to temperature abuse – as well as the potential risk factors, transmission routes, and major hazards involved. This hazard identification step, the initial step in formal risk assessment, could inform strategies to best manage food safety hazards in recovery in foodservice settings. More research is needed to address the insufficient data and unclear regulatory guidelines that are barriers to implementing innovative food recovery practices in US foodservice settings.

https://doi.org/10.1016/j.resconrec.2020.105304

 

Xianbin (Eric) Cheng

He is a 5th year PhD student. He earned his B.S. in Food Science from Rutgers University and a B.E. in Food Science and Engineering from South China University of Technology.

He is currently working on building Monte Carlo simulation models to investigate bulk sampling in produce fields or grain bins with the goal of improving sampling effectiveness. He also participates in research that utilizes UV-Vis-NIR spectroscopy and machine learning to rapidly classify contaminated corn kernels.

He loves eating seafood and avian species, likes traveling to places with beaches, oceans, and lots of sunshine, and owns a cat.

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Jorge Quintanilla

Jorge is a third year PhD Student from El Salvador. He earned his B.S. in Food Science from Zamorano University in Honduras. He initially became a member of the lab as an intern in spring 2018 and officially became a graduate student the following Spring 2019.  

Jorge is currently working on the validation of a larger simulation project built to develop better sampling plans in produce fields using data from experimental field trials, representative commercial field’s parameters, and literature plans. 

He enjoys playing basketball, swimming, and being in the nature. His favorite animals are horses and cows. He loves trying new foods! 

Gustavo A. Reyes

Gustavo is a third year PhD Student from Honduras. He earned his B.S in Food Science from the University of Nebraska-Lincoln.

He is currently working on developing a QMRA (Quantitative Microbial Risk Assessment) for share tables in school cafeterias. He also participates in research that validates sampling procedures in produce fields.

He enjoys playing and watching basketball, playing video games, eating all sorts of food.

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