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United States Department of Agriculture

Agricultural Research Service

Research Project: OBJECTIVE GRADING AND END-USE PROPERTY ASSESSMENT OF SINGLE KERNELS AND BULK GRAIN SAMPLES

Location: Engineering and Wind Erosion Research Unit

Title: Near-infrared spectroscopic method for the identification of Fusarium head blight damage and prediction of deoxynivalenol in single wheat kernels

Authors
item Peiris, K.H.S. -
item Pumphrey, Michael
item Dong, Yanhong -
item Maghirang, Elizabeth
item Berzonsky, William -
item Dowell, Floyd

Submitted to: Cereal Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 6, 2010
Publication Date: November 1, 2010
Repository URL: http://naldc.nal.usda.gov/download/49253/PDF
Citation: Peiris, K., Pumphrey, M.O., Dong, Y., Maghirang, E.B., Berzonsky, W., Dowell, F.E. 2010. Near-infrared spectroscopic method for the identification of Fusarium head blight damage and prediction of deoxynivalenol in single wheat kernels. Cereal Chemistry. 87(6):511-517.

Interpretive Summary: Proportion of Fusarium damaged kernels (FDK) and deoxynivalenol (DON) content in grain samples are often assessed to evaluate resistance of wheat germplasm for Fusarium Head Blight (FHB) resistance. Visual sorting of kernels to determine FDK% are laborious and subjective while chemical analysis of DON content is destructive and expensive. Therefore, a rapid instrumental technique to evaluate FDK and DON levels objectively and non-destructively is highly beneficial for the enhancement of breeding programs. The objective of this study was to develop automated single kernel near-infrared (SKNIR) spectroscopic methods for identification of FDK and for estimating DON levels of single kernels. The techniques developed could classify visually sound and fusarium damaged kernels with an accuracy of 98.8% and 99.9%, respectively. Moreover, the fusarium damaged kernel fraction could be sorted into 2 - 3 sub fractions with low to high DON levels. It was also possible to estimate DON levels of FDK fairly accurately. Compared to traditional methods, these techniques will allow analysis of grain samples for comprehensive evaluation of Fusarium resistance in breeding materials and therefore permit plant breeders and geneticists to evaluate many samples rapidly and cost effectively while retaining the seeds for generation advancement. Agronomists and plant pathologists may also use these techniques for analysis of grain samples in experiments designed for testing fungicides and/or other cultural control treatments for management of FHB disease and DON levels in wheat crops.

Technical 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 varieties 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 their toxins. It is important to estimate proportions of sound kernels and fusarium-damaged kernels (FDKs) in grain and to estimate DON levels of FDKs to objectively assess the resistance of a variety. An automated single kernel near-infrared (SKNIR) spectroscopic method for identification of FDKs and for estimating DON levels was evaluated. The SKNIR system classified visually sound and FDKs 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 by the SKNIR system as FDKs had a better correlation than visual FDK % with other FHB assessment indices such as FHB severity, FHB incidence and kernels g-1. This technique can be successfully employed to non-destructively sort kernels with fusarium damage and to estimate DON levels of those kernels. Because the method is non-destructive, seeds may be saved for generation advancement. The automated method is rapid, and the sorting of grains into several fractions depending on DON levels will provide breeders with more information compared with techniques that deliver average DON levels from bulk seed samples.

Last Modified: 7/30/2014
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