Abstract
Fusarium species infect maize crops leading to the production of fumonisin by their toxigenic members. Elimination of microbes is critical in mitigating further postharvest spoilage and toxin accumulation. The current study investigates the efficacy of a previously described multispectral 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 and 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 and 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 30–1.4%. This study demonstrates the use of multispectral sorting as a potential postharvest intervention tool for the reduction of Fusarium species and preformed 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.
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