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Proceedings Paper

Automatic detection of aflatoxin contaminated corn kernels using dual-band imagery
Author(s): Ambrose E. Ononye; Haibo Yao; Zuzana Hruska; Russell Kincaid; Robert L. Brown; Thomas E. Cleveland
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Paper Abstract

Aflatoxin is a mycotoxin predominantly produced by Aspergillus flavus and Aspergillus parasitiucus fungi that grow naturally in corn, peanuts and in a wide variety of other grain products. Corn, like other grains is used as food for human and feed for animal consumption. It is known that aflatoxin is carcinogenic; therefore, ingestion of corn infected with the toxin can lead to very serious health problems such as liver damage if the level of the contamination is high. The US Food and Drug Administration (FDA) has strict guidelines for permissible levels in the grain products for both humans and animals. The conventional approach used to determine these contamination levels is one of the destructive and invasive methods that require corn kernels to be ground and then chemically analyzed. Unfortunately, each of the analytical methods can take several hours depending on the quantity, to yield a result. The development of high spectral and spatial resolution imaging sensors has created an opportunity for hyperspectral image analysis to be employed for aflatoxin detection. However, this brings about a high dimensionality problem as a setback. In this paper, we propose a technique that automatically detects aflatoxin contaminated corn kernels by using dual-band imagery. The method exploits the fluorescence emission spectra from corn kernels captured under 365 nm ultra-violet light excitation. Our approach could lead to a non-destructive and non-invasive way of quantifying the levels of aflatoxin contamination. The preliminary results shown here, demonstrate the potential of our technique for aflatoxin detection.

Paper Details

Date Published: 27 April 2009
PDF: 11 pages
Proc. SPIE 7315, Sensing for Agriculture and Food Quality and Safety, 73150R (27 April 2009); doi: 10.1117/12.818307
Show Author Affiliations
Ambrose E. Ononye, Institute for Technology Development (United States)
Haibo Yao, Institute for Technology Development (United States)
Zuzana Hruska, Institute for Technology Development (United States)
Russell Kincaid, Institute for Technology Development (United States)
Robert L. Brown, USDA Agricultural Research Service (United States)
Thomas E. Cleveland, USDA Agricultural Research Service (United States)

Published in SPIE Proceedings Vol. 7315:
Sensing for Agriculture and Food Quality and Safety
Moon S. Kim; Shu-I Tu; Kaunglin Chao, Editor(s)

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