Fumonisin | Stasiewicz Food Safety Laboratory | Vision: A world where everyone, everywhere is food secure

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

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

Comparison between the previous study (left) that first used this single-kernel optical sorter for mycotoxin remediation (Stasiewicz et al., 2017), and this study (right) which adapted that process to sorting based on rejection of kernels with high-risk features associated with mycotoxin contamination.

Highlights

Visual features associated with mycotoxin risk were used to train a multispectral sorter.
Multispectral sorting reduced aflatoxin and fumonisin levels in maize form Ghana.
Sorting removed about half the total aflatoxin mass, most of the fumonisin mass, and less than one quarter of the maize.

Abstract

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.

DOI

https://doi.org/10.1016/j.foodcont.2023.109788

Efficacy of electron beam irradiation in reduction of mycotoxin-producing fungi, aflatoxin, and fumonisin, in naturally contaminated maize slurry

Highlights

Aflatoxins are more susceptible than fumonisins to eBeam irradiation in maize slurry.
Both Aspergillus and Fusarium are eliminated from maize slurry by eBeam dose of 5 kGy.
High dose eBeam irradiation (20 kGy) partially degrades pre-formed aflatoxins in maize slurry.

Abstract

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 preparations.

DOI

https://doi.org/10.1016/j.toxcx.2022.100141

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

Highlights

Uses a UV–Vis–NIR spectroscopy system to scan single corn kernels in motion.
Presents aflatoxin and fumonisin single kernel contamination distributions.
Stochastic gradient boosting best classifies single kernels by aflatoxin level.
Penalized discriminant analysis best classifies single kernels by fumonisin level.

Abstract

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 – 4.2×105 ppb), and 5.0% (n = 45) kernels with ≥2 ppm fumonisin (range 0 – 7.0×102 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

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

Abstract

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.

DOI

https://doi.org/10.1371/journal.pone.0236668

A Review of the Methodology of Analyzing Aflatoxin and Fumonisin in Single Corn Kernels and the Potential Impacts of These Methods on Food Security

Abstract

Current detection methods for contamination of aflatoxin and fumonisin used in the corn industry are based on bulk level. However, literature demonstrates that contamination of these mycotoxins is highly skewed and bulk samples do not always represent accurately the overall contamination in a batch of corn. Single kernel analysis can provide an insightful level of analysis of the contamination of aflatoxin and fumonisin, as well as suggest a possible remediation to the skewness present in bulk detection. Current literature describes analytical methods capable of detecting aflatoxin and fumonisin at a single kernel level, such as liquid chromatography, fluorescence imaging, and reflectance imaging. These methods could provide tools to classify mycotoxin contaminated kernels and study potential co-occurrence of aflatoxin and fumonisin. Analysis at a single kernel level could provide a solution to the skewness present in mycotoxin contamination detection and offer improved remediation methods through sorting that could impact food security and management of food waste.

DOI

https://doi.org/10.3390/foods9030297

(This article belongs to the Special Issue Safeguarding the Global Food Supply: Advances in Mycotoxin Prevention, Surveillance and Mitigation)