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United States Department of Agriculture

Agricultural Research Service

Research Project: OPTICAL PROPERTIES OF SMALL GRAINS FOR RAPID AND OBJECTIVE ASSESSMENT OF QUALITY AND SAFETY Title: Enhancement of Fusarium Head Blight Detection in Free-Falling Wheat Kernels Using a Bichromatic Pulsed LED Design

Authors
item Yang, I-Chang - NATIONAL TAIWAN UNIV.
item Delwiche, Stephen
item Chen, Suming - NATIONAL TAIWAN UNIV.
item Lo, Y. Martin - U.OF MD,COLLEGE PARK,MD

Submitted to: Optical Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 6, 2009
Publication Date: February 1, 2009
Citation: Yang, I., Delwiche, S.R., Chen, S., Lo, Y. 2009. Enhancement of Fusarium Head Blight Detection in Free-Falling Wheat Kernels Using a Bichromatic Pulsed LED Design. Optical Engineering. 48(2):023602-1-10.

Interpretive Summary: One of the most prevalent fungal diseases that affect small cereal grain crops during development is Fusarium Head Blight (FHB). In addition to yield reduction, this disease is problematic because it produces a mycotoxin, deoxynivalenol or DON, which is harmful to humans and livestock. Short of eradicating the disease (unlikely in the near term) or developing resistant varieties (an ongoing multi-institutional effort), grain handlers and mills are forced to deal with this problem by a series of costly options: discarding of contaminated lots, blending of contaminated grain with clean grain to reduce the mycotoxin concentration, or physical removal of mold-damaged kernels. The present research addresses the last aforementioned option and attempts to do this by means of high-speed optical inspection, one kernel at a time. A unique form of lighting was used in a two-color, or bichromatic design. Red and green light were flashed in sequence, and reflected energy readings from the kernels in free-fall were captured at high frequency (1,000-10,000 Hz range). The relationship between the red and green reflected light, which is indicative of the kernel’s color, size, and texture, was used to categorize kernels (sound vs diseased) allowing for acceptance or rejection of individual kernels in a high-speed optical sorter. Two enhancements to the system were enacted: the first being the placement of the reflected energy probe at an oblique angle with respect to the falling kernel, and the second being the mathematical processing of one color’s reflected energy signal with respect to the other color’s signal. These enhancements have resulted in increased accuracy in detection of diseased kernels from approximately 80 percent (as previously reported) to greater than 90 percent. These findings can lead to improvements of high-speed optical commercial sorters, such that mills and grain terminals will have a better means to clean up mold-contaminated grain lots.

Technical Abstract: Fusarium Head Blight is a worldwide disease of small cereals grains such as wheat. The disease is food safety concern because it produces the metabolite, deoxynivalenol (DON), which is moderately toxic to humans and non-ruminant animals. The current study was implemented to develop more efficient methods for optically recognizing Fusarium-damaged (scabby) kernels from normal (sound) wheat kernels. By development of a high-powered pulsed LED (green and red) system, it was found that Fusarium-damaged and normal individual wheat kernels can be categorized. Two parameters (slope and r-squared) from a regression analysis of the green response onto the red response were used as input parameters in linear discriminant analysis. The factors that affect the level of accuracy are the orientation of the optical probe with respect to the LED illumination source that strikes the free-falling kernel, the color contrast between normal and scabby kernels, and the manner in which one LED’s response is time-matched to the other LED. Whereas commercial high-speed optical sorters are on average 50 percent efficient at removing mold-damaged kernels, under more carefully controlled, kernel-at-rest, conditions in the laboratory, this efficiency can rise to 95 percent or better. The current research on free-falling kernels has demonstrated accuracies (>90%) that approach those of controlled conditions. Knowledge gained from this research will provide design criteria for improvement of high-speed optical sorters for DON reduction.

Last Modified: 7/30/2014