Low-cost grain sorting technologies to reduce mycotoxin contamination in maize and groundnut

Meriem Aoun; William Stafstrom; Paige Priest; John Fuchs; Gary L Windham; W Paul Williams; Rebecca J Nelson

doi:10.1016/j.foodcont.2020.107363

Low-cost grain sorting technologies to reduce mycotoxin contamination in maize and groundnut

Food Control. 2020 Dec:118:107363. doi: 10.1016/j.foodcont.2020.107363.

Authors

Meriem Aoun¹, William Stafstrom¹, Paige Priest², John Fuchs³, Gary L Windham⁴, W Paul Williams⁴, Rebecca J Nelson¹

Affiliations

¹ School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.
² Masters of Public Health Program, Cornell University, Ithaca, NY, 14853, USA.
³ The Widget Factory, Ithaca, NY, 14850, USA.
⁴ USDA, Agricultural Research Service, Corn Host Plant Resistance Research Unit, Mississippi State, MS, 39762, USA.

Abstract

The widespread contamination of foods by mycotoxins continues to be a public health hazard in sub-Saharan Africa, with maize and groundnut being major sources of contamination. This study was undertaken to assess the hypothesis that grain sorting can be used to reduce mycotoxin contamination in grain lots by removing toxic kernels. We tested a set of sorting principles and methods for reducing mycotoxin levels in maize and groundnut from a variety of genotypes and environments. We found that kernel bulk density (KBD) and 100-kernel weight (HKW) were associated with the levels of aflatoxins (AF) and fumonisins (FUM) in maize grain. A low-cost sorter prototype (the 'DropSort' device) that separated maize grain based on KBD and HKW was more effective in reducing FUM than AF. We then evaluated the effectiveness of DropSorting when combined with either size or visual sorting. Size sorting followed by DropSorting was the fastest method for reducing FUM to under 2 ppm, but was not effective in reducing AF levels in maize grain to under 20 ppb, especially for heavily AF-contaminated grain. Analysis of individual kernels showed that high -AF maize kernels had lower weight, volume, density, length, and width and higher sphericity than those with low AF. Single kernel weight was the most significant predictor of AF concentration. The DropSort excluded kernels with lower single kernel weight, volume, width, depth, and sphericity. We also found that visual sorting and bright greenish-yellow fluorescence sorting of maize single kernels were successful in separating kernels based on AF levels. For groundnut, the DropSort grouped grain based on HKW and did not significantly reduce AF concentrations, whereas size sorting and visual sorting were much more effective.

Keywords: Aflatoxins; Food safety; Fumonisins; Grain sorting; Groundnut; Maize.