Aoun Meriem, Stafstrom William, Priest Paige, Fuchs John, Windham Gary L, Williams W Paul, Nelson Rebecca J
School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.
Masters of Public Health Program, Cornell University, Ithaca, NY, 14853, USA.
Food Control. 2020 Dec;118:107363. doi: 10.1016/j.foodcont.2020.107363.
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.
霉菌毒素对食品的广泛污染在撒哈拉以南非洲地区仍然是一个公共卫生危害，玉米和花生是主要的污染源。本研究旨在评估通过去除有毒籽粒，谷物筛选能否用于降低谷物批次中霉菌毒素污染这一假设。我们测试了一套用于降低来自不同基因型和环境的玉米和花生中霉菌毒素水平的筛选原则和方法。我们发现籽粒容重（KBD）和百粒重（HKW）与玉米籽粒中黄曲霉毒素（AF）和伏马毒素（FUM）的水平相关。一种基于KBD和HKW分离玉米籽粒的低成本筛选原型设备（“DropSort”装置）在降低FUM方面比降低AF更有效。然后，我们评估了DropSorting与尺寸筛选或视觉筛选相结合时的效果。先进行尺寸筛选再进行DropSorting是将FUM降低到2 ppm以下的最快方法，但在将玉米籽粒中的AF水平降低到20 ppb以下方面无效，特别是对于AF严重污染的籽粒。对单个籽粒的分析表明，高AF玉米籽粒比低AF籽粒重量、体积、密度、长度和宽度更低，球形度更高。单粒重是AF浓度最显著的预测指标。DropSort排除了单粒重、体积、宽度、深度和球形度较低的籽粒。我们还发现，对玉米单粒进行视觉筛选和亮黄绿色荧光筛选能够成功地根据AF水平分离籽粒。对于花生，DropSort根据HKW对籽粒进行分组，并没有显著降低AF浓度，而尺寸筛选和视觉筛选则更有效。
