Rapid single kernel analysis could enable physical sorting to remove mycotoxins from bulk grains. The purpose of this study was to use visual characteristics previously associated with aflatoxin and fumonisin contamination of maize kernels to calibrate a multi-spectral sorter and then sort mycotoxin contaminated lots. A total of 76 corn samples were collected from poultry farmers in the Dorma-Ahenkro area, Ghana. Paired 400-g subsamples were used for bulk analysis and single kernel sorting. Individual kernels were selected from contaminated samples by 2 levels of stratification: visible high-risk kernels (n = 1000) and visible low risk kernels (n = 1000). High-risk kernels had one or more of 3 features: fluorescence under UV (366 nm) light, mold, or brokenness. Kernels were used to calibrate a multi-spectral sorter (individual wavelengths from 470 to 1070 nm) to remove high-risk kernels. Then, kernel samples were sorted and the reject and accept streams individually ground and tested for aflatoxin and fumonisin contamination using ELISA. Bulk sample levels ranged between 0.78 and 67 ppb aflatoxin and <2.5 × 10−3 – 5.7 ppm fumonisin. Classification algorithms to reject visible high-risk spectra were 63% sensitive and 90% specific. After sorting, samples showed a significant aflatoxin reduction (p < 0.001, 73/76 samples reduced, mean reduction 31 ppb, range −9.7 – 67 ppb); all samples showed a significant fumonisin reduction (p < 0.001, mean reduction 1.9 ppm, range 9.3 × 10−2 – 6.1 ppm). From the accepted stream, 61/76 samples tested <15 ppb aflatoxin, significantly more than the 40/76 prior to sorting (p < 0.001); all accepted samples tested <2 ppm fumonisin concentration compared to only 2/76 prior to sorting. From sorting, average mass rejected was 12% (range 1.2%–36%), and that rejected mass contained an average of 46% of the total aflatoxin (range 4.3%–97%) and 88% of the total fumonisin (range 10%–84%). Visual characteristics associated with mycotoxin contamination can inform classification models which can enable sorting contaminated maize to reduce aflatoxin and fumonisin contamination.
A 5 Month Project Funded by Sustainable Agriculture Research and Education (SARE):
The goal of this project is to provide guidance to farmers, millers, and distillers about whether and at what levels DON contaminated grain can be safely used in distillation, thus impacting farmer, miller, and distiller decision-making in selling and using these grains.
We will do this by:
Collecting wheat samples that have tested high for DON.
Distilling from contaminated wheat.
Tracing where DON segregates by Enzyme-Linked Immunosorbent Assay (ELISA) kit.
This project would help prevent the loss of both partial and full wheat crop value due to food safety contamination by converting grain to safe alternative profitable uses.
A project funded by Center for Produce Safety (CPS). Renewable for up to three years.
The produce industry needs a model to (i) identify the most important risks in a supply chain and (ii) identify which practices and control strategies appropriately reduce risks of contamination events that could lead to product recalls and illness outbreaks. This could mean which pathogen is most important for a commodity, or which practice represents the largest risk for a given supply chain.
We do this by:
Modelling the risk in a supply chain for leafy greens contaminated by two important pathogens, either Shiga toxin–producing Escherichia coli or Listeria monocytogenes.
Expanding the model to accommodate additional pathogens, practices, and commodities to assess the impact of newly identified risks such as newly identified problematic practices, emerging pathogens, or products.
Measuring the impact of newly identified risks or newly modeled control strategies on how they change the total supply chain risk as compared to the risk uncontrolled by current practices.
Maize is a staple food in Kenya. However, maize is prone to fungal infestation, which may result in production of harmful aflatoxins and fumonisins. Electron beam (eBeam) food processing is a proven post-harvest technology, but published literature is rare on the ability of eBeam to reduce mycotoxins in naturally contaminated maize samples. This study evaluated the efficacy of eBeam doses in reducing viable fungal populations and the destruction of aflatoxins and fumonisins in naturally highly contaminated maize samples from eastern Kenya. Ninety-seven maize samples were analyzed for total aflatoxins and fumonisins using commercial ELISA kits. Then, 24 samples with >100 ng/g of total aflatoxins and >1000 ng/g of total fumonisins were chosen for eBeam toxin degradation studies. Prior to eBeam exposure studies, the samples were made into a slurry using sterile de-ionized water. These slurry samples were exposed to target doses of 5 kGy, 10 kGy, and 20 kGy, with 0 kGy (untreated) samples as controls. Samples were analyzed for total fungal load using culture methods, the quantity of total aflatoxins and fumonisins using ELISA, and the presence of Aspergillus and Fusarium spp. nucleic acids using qPCR for just control samples. There was a significant positive correlation in the control samples between total Aspergillus and aflatoxin levels (r = 0.54; p = 0.007) and total Fusarium and fumonisin levels (r = 0.68; p < 0.001). Exposure to eBeam doses 5 kGy and greater reduced fungal loads to below limits of detection by plating (<1.9 log(CFU/g)). There was also a significant (p = 0.03) average reduction of 0.3 log (ng/g) in aflatoxin at 20 kGy (range from −0.9 to 1.4 log (ng/g)). There was no significant reduction in fumonisin even at 20 kGy. eBeam doses below 20 kGy did not reduce mycotoxins. These results confirm the sensitivity of fungi to eBeam doses in a naturally contaminated maize slurry and that 20 kGy is effective at degrading some pre-formed aflatoxin in such maize preparation
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.
Our group will colaborate with PI (Yi-Cheng Wang, profile) on a 2 year project funded by USDA NIFA
The long-termgoal of this project is to develop a revolutionary, yet inexpensive and easy-to-use, decontamination technique, whereby retailers and other stakeholders can readily minimize cross-contamination and improve food safety. This will ultimately benefit the American people by increasing the availability and accessibility of safe and nutritious food. However, additional research must be done if we are to understand and better exploit this emerging technology. The objectives on the way to achieving this goal are as follows.
Evaluate the efficacy of far-UVC light for inactivating bacteria in buffer and on food-contact surfaces
Evaluate the efficacy of microplasma-based far-UVC light for decontaminating real foods.
Evaluate the quality of foods before and after far-UVC light treatment
Ruben is Postdoctoral Researcher from Ecuador. He earned his B.S. in Chemistry from The University of Tampa.
He is currently developing a Near Infrared Red/ Visible Light/ Ultraviolet light spectrometer to detect aflatoxin and fumonisin in corn. His research goal is to create a sorting platform with a spectrometer capable of removing mycotoxin contaminated corn kernels and minimize food waste while improving food security.
He loves to practice sports, especially soccer, and play videogames with his friends. He is a food lover, and in his leisure, he likes to improve his culinary skills.
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!
Cristina is a second-year PhD student from Mexico. She earned her B.S. in Food Science from the Autonomous University of Queretaro. She first came to the lab for a summer internship in 2018, then started her grad school journey in Fall 2019.
She uses a high-throughput system to test the effectiveness of novel antimicrobials, such as essential oils, against Listeria monocytogenes on deli-ham. She also focuses her research on understanding the action mechanisms of these compounds using RNA-seq.
She enjoys cooking and watching series (Especially, Grey’s Anatomy on repeat).
Gustavo is a fourth-year PhD Student from Honduras. He earned his B.S in Food Science from the University of Nebraska-Lincoln in 2017, then worked as a FS and QA specialist for two years, subsequently starting his graduate school journey in Fall 2019
He has previously worked on developing a QMRA (Quantitative Microbial Risk Assessment) for share tables in school cafeterias to predict the risk for norovirus cross-contamination on apples.
He is currently working on developing farm-to-consumer process models for leafy green and tomatoes. The goal of these process models is to assess the relative efficacy of conducting product testing at different process stages.
His hobbies are playing basketball, eating good food, trying and out new things like golf.