IDENTIFICATION AND ENHANCEMENT OF SEED-BASED BIOCHEMICAL RESISTANCE IN CROPS TO AFLATOXIN PRODUCING PATHOGENS
Location: Food and Feed Safety Research
Title: Single Aflatoxin Contaminated Corn Kernel Analysis with Fluorescence Hyperspectral Image
Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: March 11, 2010
Publication Date: April 20, 2010
Citation: Yao, H., Hruska, Z., Kincaid, R., Ononye, A., Brown, R.L., Cleveland, T.E. 2010. Single Aflatoxin Contaminated Corn Kernel Analysis with Fluorescence Hyperspectral Image. Proceedings of SPIE Conference "Sensing for Agriculture and Food Quality and Safety. 7676:p.13.
Aflatoxins are toxic secondary metabolites of the fungi Aspergillus flavus and Aspergillus parasiticus, among others. Aflatoxin contaminated corn is toxic to domestic animals when ingested in feed and is a known carcinogen associated with liver and lung cancer in humans. Consequently, aflatoxin levels in food and feed are regulated by the Food and Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food and 100 ppb in feed for interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests including thin-layer chromatography (TCL) and high performance liquid chromatography (HPLC). These analytical tests require the destruction of samples, and are costly and time consuming. Thus, the ability to detect aflatoxin in a rapid, non-destructive way is crucial to the grain industry, particularly to corn industry. Hyperspectral imaging technology offers a non-invasive approach toward screening for food safety inspection and quality control based on its spectral signature. The focus of this paper is to classify aflatoxin contaminated single corn kernels using fluorescence hyperspectral imagery. Field inoculated corn kernels were used in the study. Contaminated and control kernels under long wavelength ultraviolet excitation were imaged using a visible near-infrared (VNIR) hyperspectral camera. The imaged kernels were chemically analyzed to provide reference information for image analysis. This paper describes a procedure to process corn kernels located in different images for statistical training and classification. Two classification algorithms, Maximum Likelihood and Binary Encoding, were used to classify each corn kernel into “control” or “contaminated” through pixel classification. The Binary Encoding approach had a slightly better performance with accuracy equals to 87% or 88% when 20 ppb or 100 ppb was used as classification threshold, respectively.