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ARS Home » Southeast Area » New Orleans, Louisiana » Southern Regional Research Center » Food and Feed Safety Research » Research » Publications at this Location » Publication #288397
Title: Hyperspectral image classification and development of fluorescence index for single corn kernels infected with Aspergillus flavus

Author
item YAO, HAIBO - Mississippi State University
item HRUSKA, ZUZANA - Mississippi State University
item KINCAID, RUSSELL - Mississippi State University
item Brown, Robert
item Bhatnagar, Deepak
item Cleveland, Thomas

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/10/2013
Publication Date: 11/10/2013
Citation: Yao, H., Hruska, Z., Kincaid, R., Brown, R.L., Bhatnagar, D., Cleveland, T.E. 2013. Hyperspectral image classification and development of fluorescence index for single corn kernels infected with Aspergillus flavus. Transactions of the ASABE. 56(5):1977-1988.

Interpretive Summary: Aflatoxins are poisons produced by the fungus Aspergillus flavus after it infects agricultural commodities such as corn. Since aflatoxins in food and feed are regulated, enhanced ability to detect and measure fungal growth and aflatoxin contamination of corn could contribute significantly towards the separation of contaminated from healthy grain. A collaboration between ARS-SRRC, Food and Feed Safety Research Unit and Mississippi State University, Stennis Space Center, MS is exploring the use of hyperspectral imaging non-destructive technology to detect mycotoxin-producing fungi in grain products. The purpose of this study was to classify aflatoxin contaminated single corn kernels using fluorescence hyperspectral imagery. Field inoculated corn kernels were used in the study and kernels to be imaged were classified as either contaminated (20 ppb or 100 ppb level) or control after chemical analysis. Two classification algorithms were tested, and one proved superior to the other in accuracy. Also two key wavelengths were determined in this study. Further experiments may lead to this technology being used to rapidly and accurately detect/measure aflatoxin contamination of corn without destruction of healthy grain. This could provide a useful tool to both growers and buyers in the corn industry that could enhance protection of food and feed as well as increase profits.

Technical Abstract: Aflatoxins are toxic secondary metabolites predominantly produced by the fungi Aspergillus flavus and A. parasiticus. 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 spectral signatures. The focus of this paper was 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 for processing 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 equal to 87% or 88% when 20 ppb or 100 ppb was used as classification threshold, respectively. In addition, three narrow-band fluorescence indices were developed and tested in this study. It was found that the highest correlation was -0.81 with the Normalize Difference Fluorescence Index (NDFI). The two bands used for the NDFI were 437 and 537 nm. The use of key wavelengths for contamination detection would be helpful for developing rapid and non-invasive inspection systems. This study demonstrated the potential of using fluorescence hyperspectral imagery for aflatoxin contamination detection in corn kernels infected with A. flavus.