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United States Department of Agriculture

Agricultural Research Service

Research Project: FUNGAL ENDOPHYTES OF MAIZE: GENE PRODUCTS CONFERRING RESISTANCE TO AFLATOXIN AND FUMONISIN Title: DETECTION AND REMOVAL OF SINGLE MYCOTOXIN CONTAMINATED MAIZE GRAINS FOLLOWING HARVEST

Authors
item Wicklow, Donald
item Pearson, Thomas

Submitted to: Stored Products Protection International Working Conference Proceedings
Publication Type: Proceedings
Publication Acceptance Date: October 1, 2006
Publication Date: October 1, 2006
Citation: Wicklow, D.T., Pearson, T.C. 2006. Detection and removal of single mycotoxin contaminated maize grains following harvest. Proceedings of the 9th International Working Conference on Stored Product Protection. p. 109-119.

Interpretive Summary: Grains highly contaminated by aflatoxin and fumonisin are unevenly distributed in a grain lot and may be concentrated in a very small percentage of the product. Our research seeks to simultaneously eliminate both aflatoxin and fumonisin-contaminated kernels in a single pass through a commercial optical sorter. We have shown that a few absorbance bands (filters) in the visible and near infrared spectrum can detect whole white or yellow corn kernels highly contaminated in the field with aflatoxin and fumonisin at a low classification error rate. By removing a small percentage of contaminated kernels, instead of discarding the entire lot, it is possible to reduce aflatoxin and fumonisin contamination to satisfy statutory levels. Our research also seeks to classify individual grains infected with different fungal species and to distinguish resistance and susceptibility reactions among corn varieties. Neural networks are being trained to classify grains by fungal species using principle components of the full reflectance spectra. Our initial work has shown that classification accuracies for severely discolored grains infected with Aspergillus flavus, Stenocarpella maydis, Fusarium graminearum, Fusarium verticillioides, and Trichoderma viride averaged 92.1% and 94.8% for two commercial corn hybrids. Spectra of single maize grains can be measured automatically, and grains with multiple symptoms and mycotoxins can be sorted into different fungal species categories at rates of about 1 per second using commercial instruments.

Technical Abstract: Grains highly contaminated by aflatoxin and fumonisin are unevenly distributed in a grain lot and may be concentrated in a very small percentage of the product. Near-infrared (NIR) reflectance spectra (500-1700 nm) were analyzed to select the pair of absorbance bands (filters) giving the lowest classification error rate for removing whole yellow maize grains contaminated with aflatoxin (750 &1200 nm) or white maize grains contaminated with fumonisin (500 &1200 nm) in a single pass through a commercial high speed sorter (@ 7000 Kg/hr). Our research also seeks to classify individual grains infected with different fungal species, and to distinguish resistance and susceptibility reactions among corn varieties. Neural networks are being trained to classify grains by fungal species using principle components of the full reflectance spectra. Spectra of single maize grains can be measured automatically, and grains with multiple symptoms and mycotoxins can be sorted into different fungal species categories at rates of about 1 per second using commercial instruments. Our initial work has shown that classification accuracies for severely discolored grains infected with Aspergillus flavus, Stenocarpella maydis, Fusarium graminearum, Fusarium verticillioides, and Trichoderma viride averaged 92.1% and 94.8% for two commercial corn hybrids. Protective endophytes, including mycoparasites that live asymptomatically in maize, are not readily distinguished from uninfected grains, and represent confounding variables in maize variety trials for fungus-mycotoxin resistance.

Last Modified: 10/31/2014
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