Aggregative Soil Sampling Using Boot Covers Compared to Soil Grabs From Commercial Romaine Fields Shows Similar Indicator Organism and Microbial Community Recoveries

Aggregative boot cover sampling may be a more representative, practical, and powerful method for preharvest produce soil testing than grab sampling because boot covers aggregate soil from larger areas. Our study tests if boot cover sampling results reflect quality and safety indicator organisms and community diversity of grab sampling. We collected soil samples from commercial romaine lettuce fields spanning 5060 m2 using boot covers (n = 28, m = 1.1 ± 0.4 g; wearing boot covers and walking along the path), composite grabs (n = 28, m = 231 ± 24 g; consisting of 60 grabs of 3–5 g each), and high-resolution grabs (n = 72, m = 56 ± 4 g; taking one sample per stratum). Means and standard deviations of log-transformed aerobic plate counts (APCs) were 7.0 ± 0.3, 7.1 ± 0.2, and 7.3 ± 0.2 log(CFU/g) for boot covers, composite grabs, and high-resolution grabs, respectively. APCs did not show biologically meaningful differences between sample types. Boot covers recovered on average 0.6 log(CFU/g) more total coliforms than both grabs (p < 0.001) where means and standard deviations of log-transformed counts were 3.2 ± 1.0, 2.6 ± 0.6, and 2.6 ± 1.0 log(CFU/g) for boot covers, composite grabs, and high-resolution grabs, respectively. There were no generic E. coli detected in any sample by enumeration methods with LODs of 1.3–2.1 log(CFU/g) for boot covers and 0.5 log(CFU/g) for both grabs. By 16S rRNA sequencing, community species diversity (alpha diversity) was not significantly different within collection methods. While communities differed (p < 0.001) between soil sampling methods (beta diversity), variance in microbial communities was not significantly different. Of the 28 phyla and 297 genera detected, 25 phyla (89%) and 258 genera (87%) were found by all methods. Overall, aggregative boot cover sampling is similar to both grab methods for recovering quality and safety indicator organisms and representative microbiomes. This justifies future work testing aggregative soil sampling for foodborne pathogen detection

Multispectral Sorting Based on Visibly High-Risk Kernels Sourced from Another Country Reduces Fumonisin and Toxigenic Fusarium on Maize Kernels

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