Location: Food and Feed Safety ResearchTitle: Development of narrow-band fluorescence index for the detection of aflatoxin contaminated corn
|YAO, HAIBO - Mississippi State University|
|HRUSKA, ZUZANA - Mississippi State University|
|KINCAID, RUSSELL - Mississippi State University|
|ONONYE, AMBROSE - Mississippi State University|
Submitted to: Sensing for Agriculture and Food Quality and Safety
Publication Type: Proceedings
Publication Acceptance Date: 4/28/2011
Publication Date: 5/31/2011
Citation: Yao, H., Hruska, Z., Kincaid, R., Ononye, A., Brown, R.L., Bhatnagar, D., Cleveland, T.E. 2011. Development of narrow-band fluorescence index for the detection of aflatoxin contaminated corn. Proceedings of 2011 SPIE Conference, Sensing for Agriculture and Food Quality and Safety III, April 26-27, 2011, Orlando, FL. p.8027-12.
Technical Abstract: Aflatoxin is produced by the fungus Aspergillus flavus when the fungus invades developing corn kernels. Because of its potent toxicity, the levels of aflatoxin are regulated by the Food and Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food, and feed intended for interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests. These tests require the destruction of samples, can be costly and time consuming, and often rely on less than desirable sampling techniques. Thus, the ability to detect aflatoxin in a rapid, non-invasive way is crucial to the corn industry in particular. This paper described how narrow-band fluorescence indices were developed for aflatoxin contamination detection based on single corn kernel samples. The indices were based on two bands extracted from full wavelength fluorescence hyperspectral imagery. The two band results were later applied to two large sample experiments with 25 g and 1 kg of corn per sample. The detection accuracies were 85% and 95% when 100 ppb threshold was used. Since the data acquisition period is significantly lower for several image bands than for full wavelength hyperspectral data, this study would be helpful in the development of real-time detection instrumentation for the corn industry.