Multi-spectral kernel sorting to reduce aflatoxins and fumonisins in Kenyan maize

Stasiewicz, Matthew J., Falade, Titilayo D. O., Mutuma, Murithi, Mutiga, Samuel K., Harvey, Jagger J. W., Fox, Glen, Pearson, Tom C., Muthomi, James W. and Nelson, Rebecca J. (2017) Multi-spectral kernel sorting to reduce aflatoxins and fumonisins in Kenyan maize. Food Control, 78 203-214. doi:10.1016/j.foodcont.2017.02.038


Author Stasiewicz, Matthew J.
Falade, Titilayo D. O.
Mutuma, Murithi
Mutiga, Samuel K.
Harvey, Jagger J. W.
Fox, Glen
Pearson, Tom C.
Muthomi, James W.
Nelson, Rebecca J.
Title Multi-spectral kernel sorting to reduce aflatoxins and fumonisins in Kenyan maize
Journal name Food Control   Check publisher's open access policy
ISSN 0956-7135
1873-7129
Publication date 2017-08-01
Sub-type Article (original research)
DOI 10.1016/j.foodcont.2017.02.038
Open Access Status Not yet assessed
Volume 78
Start page 203
End page 214
Total pages 12
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon Press
Collection year 2018
Language eng
Formatted abstract
Maize, a staple food in many African countries including Kenya, is often contaminated by toxic and carcinogenic fungal secondary metabolites such as aflatoxins and fumonisins. This study evaluated the potential use of a low-cost, multi-spectral sorter in identification and removal of aflatoxin- and fumonisin-contaminated single kernels from a bulk of mature maize kernels. The machine was calibrated by building a mathematical model relating reflectance at nine distinct wavelengths (470–1550 nm) to mycotoxin levels of single kernels collected from small-scale maize traders in open-air markets and from inoculated maize field trials in Eastern Kenya. Due to the expected skewed distribution of mycotoxin contamination, visual assessment of putative risk factors such as discoloration, moldiness, breakage, and fluorescence under ultra-violet light (365 nm), was used to enrich for mycotoxin-positive kernels used for calibration. Discriminant analysis calibration using both infrared and visible spectra achieved 77% sensitivity and 83% specificity to identify kernels with aflatoxin >10 ng g−1 and fumonisin >1000 ng g−1, respectively (measured by ELISA or UHPLC). In subsequent sorting of 46 market maize samples previously tested for mycotoxins, 0–25% of sample mass was rejected from samples that previously tested toxin-positive and 0–1% was rejected for previously toxin-negative samples. In most cases where mycotoxins were detected in sorted maize streams, accepted maize had lower mycotoxin levels than the rejected maize (21/25 accepted maize streams had lower aflatoxin than rejected streams, 25/27 accepted maize streams had lower fumonisin than rejected streams). Reduction was statistically significant (p < 0.001), achieving an 83% mean reduction in each toxin. With further development, this technology could be used to sort maize at local hammer mills to reduce human mycotoxin exposure in Kenya, and elsewhere in the world, while at once reducing food loss, and improving food safety and nutritional status.
Keyword Aflatoxin
Food safety
Fumonisin
Maize
Spectral sorting
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
Queensland Alliance for Agriculture and Food Innovation
 
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