• Contact Us
    • Send Feedback
    • Login
    View Item 
    •   Home
    • Journal and Journal Articles
    • Journal and Journal Articles
    • View Item
    •   Home
    • Journal and Journal Articles
    • Journal and Journal Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    Whole Repository
    CollectionsIssue DateRegionCountryHubAffiliationAuthorsTitlesSubject
    This Sub-collection
    Issue DateRegionCountryHubAffiliationAuthorsTitlesSubject

    My Account

    Login

    Welcome to the International Institute of Tropical Agriculture Research Repository

    What would you like to view today?

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

    Thumbnail
    View/Open
    S17ArtStasiewiczMultispectralInthomDev.pdf (876.8Kb)
    Date
    2017-08-15
    Author
    Stasiewicz, Matthew
    Falade, T.D.O.
    Mutuma, M.
    Mutiga, Samuel
    Harvey, Jagger J.W.
    Fox, Glen
    Pearson, T.C.
    Muthomi, J.W.
    Nelson, Rebecca
    Type
    Journal Article
    Target Audience
    Scientists
    Metadata
    Show full item record
    Abstract/Description
    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.
    http://dx.doi.org/10.1016/j.foodcont.2017.02.038
    Multi standard citation
    Permanent link to this item
    https://hdl.handle.net/20.500.12478/1597
    Digital Object Identifier (DOI)
    http://dx.doi.org/10.1016/j.foodcont.2017.02.038
    Agrovoc Terms
    Aflatoxins; Food Safety
    Regions
    Africa; East Africa
    Countries
    Kenya
    Journals
    Food Control
    Collections
    • Journal and Journal Articles4842
    copyright © 2019  IITASpace. All rights reserved.
    IITA | Open Access Repository