The fungus Aspergillus flavus naturally grows on corn and other crops and produces a toxic chemical by-product, aflatoxin. Fungal growth, and hence aflatoxin problems, are more common in hot and humid climates, such as the southeastern US. Environmental conditions, crop health, harvest methods, and storage conditions can affect incidence and degree of aflatoxin contamination. <P>Aflatoxin-contaminated grain is toxic to animals, particularly young animals, reproductively active animals, and poultry. The United States Food and Drug Administration has established guidelines for acceptable levels of aflatoxin-contamination for specific uses, therefore, grain-handling facilities routinely test loads before accepting delivery. Conventional approaches to aflatoxin detection utilize long-wave ultraviolet light illumination as a screening method to detect alflatoxin producing fungus, followed by chemical analysis to determine aflatoxin concentration. UV detection methods do not actually detect mycotoxins, but rather a metabolic product of the mycotoxin producing fungus, A. Flavus. Consequently, UV methods are non-specific and of limited value, except for initial screening. <P>Chemical detection methods are time-consuming and destructive of the sampled grain. The proposed mycotoxin detection project is designed to develop, validate, and commercialize a rapid, non-destructive technology for mycotoxin contamination detection in grains. <P>The ultimate goal of the project is to use non-invasive active hyperspectral imaging techniques (a specialized method that uses measurement of the reflections from lights of differing wavelength) to detect and quantify mycotoxins produced by various molds on grains, in real-time. <P>The specific goals are: <OL> <LI> Identify and quantify aflatoxin-producing fungi on corn, using a non-destructive hyperspectral imaging system. <LI> Produce spectral libraries for fungus alone and in infected corn. <LI> Determine spectral differences between different corn varieties, resistant and susceptible to aflatoxin contamination and infected and un-infected with aflatoxin producing fungi. <LI> Develop rapid, non-destructive hyperspectral imaging methodology to measure fungal growth and aflatoxin in corn kernels and spectral signatures associated with traits for resistance to fungal infection and aflatoxin contamination in corn kernels. Test system's effectiveness in laboratory and field situations.
<p>The aflatoxin detection project is designed for the development of rapid, non-destructive technologies for aflatoxin detection in grains. Because of their potent carcinogenic properties, certain mycotoxins pose a major health threat and prove to be costly to the grain producing and processing industry. The Federal Drug Administration issued stringent regulations for mycotoxin content for grains intended for export as well as domestic use for both humans and animals. The intended outcomes of this project include: spectral corn kernel identification for different corn varieties; development of spectral libraries for fungus alone and in infected corn; development of rapid, non-destructive techniques for the detection of aflatoxin contaminated corn. </p><P> Approach: Corn kernel varieties with varying levels of resistance to aflatoxin producing fungi will be collected and imaged using a tabletop hyperspectral scanning imaging system. Kernels will be spectrally analyzed to determine how much the UV, visible, and near infrared portions of the electromagnetic spectrum differ from one corn variety to another. Cultures of aflatoxin producing and non-producing fungi will also be imaged and the spectral fingerprints will be collected to produce a "spectral library" of the different strains of fungi. These data will be used to determine if hyperspectral imaging can then be used to differentiate and quantify the varying fungal strains and/or their aflatoxin production both in pure fungal culture and in fungal-infected kernels from corn varieties either resistant or susceptible to aflatoxin contamination. Techniques also will be investigated during ongoing experiments to determine the best imaging environment in which to accomplish hyperspectral analyses, such as type and direction of lighting. Once appropriate algorithms are developed, the system will be tested in various laboratory and field experiments to determine the efficacy of the system. </P>