Automatic detection of aflatoxin contaminated corn kernels using dual-band imagery

Ambrose E. Ononye; Haibo Yao; Zuzana Hruska; Russell Kincaid; Robert L. Brown; Thomas E. Cleveland

doi:10.1117/12.818307

27 April 2009 Automatic detection of aflatoxin contaminated corn kernels using dual-band imagery

Ambrose E. Ononye, Haibo Yao, Zuzana Hruska, Russell Kincaid, Robert L. Brown, Thomas E. Cleveland

Proceedings Volume 7315, Sensing for Agriculture and Food Quality and Safety; 73150R (2009) https://doi.org/10.1117/12.818307
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Ambrose E. Ononye, Haibo Yao, Zuzana Hruska, Russell Kincaid, Robert L. Brown, and Thomas E. Cleveland "Automatic detection of aflatoxin contaminated corn kernels using dual-band imagery", Proc. SPIE 7315, Sensing for Agriculture and Food Quality and Safety, 73150R (27 April 2009); https://doi.org/10.1117/12.818307

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