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Near-Infrared Spectroscopic Method for Identification of Fusarium Head Blight Damage and Prediction of Deoxynivalenol in Single Wheat Kernels

November 2010 Volume 87 Number 6
Pages 511 — 517
K. H. S. Peiris,1 M. O. Pumphrey,2 Y. Dong,3 E. B. Maghirang,4 W. Berzonsky,5 and F. E. Dowell6,7

Kansas State University, Biological and Agricultural Engineering Department Manhattan, KS. USDA-ARS CGAHR, Hard Winter Wheat Genetics Research Unit, Manhattan, KS. University of Minnesota, Department of Plant Pathology, St. Paul, MN. USDA ARS CGAHR, EWERU, Manhattan, KS. SDSU, Plant Science, Brookings, SD. USDA ARS CGAHR, EWERU, Manhattan, KS. Corresponding author. E-mail: Floyd.Dowell@ars.usda.gov


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Accepted July 6, 2010.
ABSTRACT

Fusarium Head Blight (FHB), or scab, can result in significant crop yield losses and contaminated grain in wheat (Triticum aestivum L.). Growing less susceptible cultivars is one of the most effective methods for managing FHB and for reducing deoxynivalenol (DON) levels in grain, but breeding programs lack a rapid and objective method for identifying the fungi and toxins. It is important to estimate proportions of sound kernels and Fusarium-damaged kernels (FDK) in grain and to estimate DON levels of FDK to objectively assess the resistance of a cultivar. An automated single kernel near-infrared (SKNIR) spectroscopic method for identification of FDK and for estimating DON levels was evaluated. The SKNIR system classified visually sound and FDK with an accuracy of 98.8 and 99.9%, respectively. The sound fraction had no or very little accumulation of DON. The FDK fraction was sorted into fractions with high or low DON content. The kernels identified as FDK by the SKNIR system had better correlation with other FHB assessment indices such as FHB severity, FHB incidence and kernels/g than visual FDK%. This technique can be successfully employed to nondestructively sort kernels with Fusarium damage and to estimate DON levels of those kernels. Single kernels could be predicted as having low (<60 ppm) or high (>60 ppm) DON with ≈96% accuracy. Single kernel DON levels of the high DON kernels could be estimated with R2 = 0.87 and standard error of prediction (SEP) of 60.8 ppm. Because the method is nondestructive, seeds may be saved for generation advancement. The automated method is rapid (1 kernel/sec) and sorting grains into several fractions depending on DON levels will provide breeders with more information than techniques that deliver average DON levels from bulk seed samples.



This article is in the public domain and not copyrightable. It may be freely reprinted with customary crediting of the source. AACC International, Inc., 2010.