Spectral kernel sorting based on high-risk visual features associated with mycotoxin contamination reduces aflatoxin and fumonisin contamination in maize from Ghana


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.

Single kernel aflatoxin and fumonisin contamination distribution and spectral classification in commercial corn


Aflatoxin and fumonisin contamination distribution in corn is non-homogeneous. Therefore, bulk sample testing may not accurately represent the levels of contamination. Single kernel analysis could provide a solution to these problems and lead to remediation strategies such as sorting. Our study uses extensive single kernel aflatoxin (AF) and fumonisin (FM) measurements to (i) demonstrate skewness, calculate weighted sums of toxin contamination for a sample, and compare those values to bulk measurements, and (ii) improve single kernel classification algorithm performance. Corn kernels with natural contamination of aflatoxin and fumonisin (n = 864, from 9 bulk samples) were scanned individually twice for reflectance between the ultraviolet–visible–near infrared spectrum (304 nm–1086 nm), then ground and measured for aflatoxin and fumonisin using ELISA. Single kernel contamination distribution was non-homogeneous with 1.0% (n = 7) of kernels with ≥20 ppb aflatoxin (range 0 – 4.2×10^5 ppb), and 5.0% (n = 45) kernels with ≥2 ppm fumonisin (range 0 – 7.0×10^2 ppm). A single kernel weighted sum was calculated and compared to bulk measurements. Average difference in mycotoxin levels (AF = 0.0 log(ppb), FM = 0.0 log(ppm), weighted sum – measured bulk levels) calculated no systematic bias between the two methods, though with considerable range of −1.4 to 0.7 log(ppb) for AF and −0.6 to 0.8 log(ppm) for FM. Algorithms were trained on 70% of the kernels to classify aflatoxin (≥20ppb) and fumonisin (≥2ppm), while the remaining 30% of kernels were used for testing. For aflatoxin, the best performing algorithm was stochastic gradient boosting model with an accuracy of 0.83 (Sensitivity (Sn) = 0.75, Specificity (Sp) = 0.83), for both training and testing set. For fumonisin, the penalized discriminant analysis outperformed the rest of the algorithms, with a training accuracy of 0.89 (Sn = 0.87, Sp = 0.88), and testing accuracy of 0.86 (Sn = 0.78, Sp = 0.87). The present study improves the foundations for single kernel classification of aflatoxin and fumonisin in corn, and can be applied to high throughput screening. This study demonstrates the heterogeneous distribution of aflatoxin and fumonisin contamination at single kernel level, comparing bulk levels calculated from those data to traditional bulk tests, and utilizing a UV–Vis–NIR spectroscopy system to classify single corn kernels by aflatoxin and fumonisin level.

DOI: https://doi.org/10.1016/j.foodcont.2021.108393

Digital farm-to-facility food safety testing optimization

A two year project funded by the:

With the following 4 objectives:

  • Build a Field-to-Facility generic supply chain model of produce safety testing.
  • Adapt the supply chain and collect parameters to represent a variety of commodities with distinct risk profiles and risk-management options.
  • Optimize testing across the supply chain of each commodity incorporating representative testing programs at primary production, harvesting, receiving, processing, and packing and assessing their impact to manage safety.

More information on the project can be found in the funder’s database.

When to use one-dimensional, two-dimensional, and Shifted Transversal Design pooling in mycotoxin screening


While complex sample pooling strategies have been developed for large-scale experiments with robotic liquid handling, many medium-scale experiments like mycotoxin screening by Enzyme-Linked Immunosorbent Assay (ELISA) are still conducted manually in 48- and 96-well plates. At this scale, the opportunity to save on reagent costs is offset by the increased costs of labor, materials, and risk-of-error caused by increasingly complex pooling strategies. This paper compares one-dimensional (1D), two-dimensional (2D), and Shifted Transversal Design (STD) pooling to study whether pooling affects assay accuracy and experimental cost and to provide guidance for when a human experimentalist might benefit from pooling. We approximated mycotoxin contamination in single corn kernels by fitting statistical distributions to experimental data (432 kernels for aflatoxin and 528 kernels for fumonisin) and used experimentally-validated Monte-Carlo simulation (10,000 iterations) to evaluate assay sensitivity, specificity, reagent cost, and pipetting cost. Based on the validated simulation results, assay sensitivity remains 100% for all four pooling strategies while specificity decreases as prevalence level rises. Reagent cost could be reduced by 70% and 80% in 48- and 96-well plates, with 1D and STD pooling being most reagent-saving respectively. Such a reagent-saving effect is only valid when prevalence level is < 21% for 48-well plates and < 13%-21% for 96-well plates. Pipetting cost will rise by 1.3–3.3 fold for 48-well plates and 1.2–4.3 fold for 96-well plates, with 1D pooling by row requiring the least pipetting. Thus, it is advisable to employ pooling when the expected prevalence level is below 21% and when the likely savings of up to 80% on reagent cost outweighs the increased materials and labor costs of up to 4 fold increases in pipetting.


Improving Microbial Food Safety Through Engineering and Statistical Approaches in Food Microbiology

A 5-year project funded by USDA NIFA Hatch funds which represented a significant portion of my group’s startup funds.


To support the long-term goal of developing a flexible applied food safety laboratory, Hatch funds will be used to support the following initial, discrete projects:

  • Genomics and Engineering Tools for Persistent Pathogen Identification and Control.
  • Single-Kernel Sorting to Remove Mycotoxins from Cereals.
  • Systems Approaches to Valuing Reductions in Foodborne Pathogen Contamination of Foods.

More information can be found on the funder’s database.


Simulating Large-Number Bulk-Product Sampling to Improve Food Safety Sampling Plans

A 2-year externally funded project by:

This project will build a simulation model to solve the problem of how to best take many samples of bulk products for food safety testing. That way, industries can use that knowledge to create sampling plans, and sampling devices, for their products that achieve important food safety goals. The general objective of this project is to build a validated and ready-to-use simulation model of bulk product sampling to improving sampling plans. The specific aims are:

  • Specific Aim 1. Build a bulk product sampling simulation model for food safety testing.
  • Specific Aim 2. Validate the simulation model against reported bulk product sampling.
  • Specific Aim 3. Validate corn-aflatoxin simulation against experimental Texas corn sampling.
  • Specific Aim 4. Build a user interface to parameterize the simulation model by answering a series of web-based questions.

ILSI, and my group, is committed to open, honest science particularly when industry stakeholder come together to aggregate support for important projects such as this.  Therefore, you can find a complete pre-registration of this project under the Open Science Framework:

Project Public Registration

And additional information on the funder’s website of projects.

There is a video update of a poster presentation at IAFP 2019 here