DisplayTitle

Identification of Wheat Lines Possessing the 1AL.1RS or 1BL.1RS Wheat-Rye Translocation by Near-Infrared Reflectance Spectroscopy

¹U.S. Department of Agriculture, Agricultural Research Service, Instrumentation and Sensing Laboratory, Building 303, BARC-East, Beltsville, MD 20705-2350. Names are necessary to report factually on available data; however, the USDA neither guarantees nor warrants the standard of the product, and the use of the name by the USDA implies no approval of the product to the exclusion of others that may also be suitable. ²Corresponding author. E-mail: sdelwiche@asrr.arsusda.gov ³USDA-ARS, Department of Agronomy, University of Nebraska East Campus, Lincoln, NE. ⁴Current address: Department of Crop and Soil Science, Oregon State University, Corvallis, OR.

Wheat-rye chromosomal translocations, particularly those involving the short arm of rye chromosome 1R, have been used during the past 25 years to instill resistance to plant pathogens and insects and improve the hardiness, adaptation, and yield of wheat. Unfortunately, the presence of the 1AL.1RS or 1BL.1RS rye translocations in wheat has been shown to impart inferior dough handling and baking characteristics. Although numerous analytical techniques (e.g., HPLC, monoclonal antibody tests, high-performance capillary electrophoresis) have been developed for detecting these translocations, the complexity of the analytical procedures restricts their use to research and analytical laboratories. The purpose of this study was to examine the potential of diffuse reflectance near-infrared spectroscopy, a well-accepted technique in the grain industry, for detecting 1RS-containing genotypes. This research used three independent groups of wheat samples, ranging in genetic diversity from sister lines derived from 1RS breeding populations to commercial cultivars. Based on the diffuse reflectance spectra (1,100–2,500 nm) of flour, partial least squares (PLS) models, through cross-validation, exhibited misclassification rates as low as 0%, particularly for commercial cultivars. Misclassification rates for corresponding, but separate, test sets were as low as 1%. When the same modeling procedure was applied to samples of more closely related genetic backgrounds, cross-validation misclassification rates rose to 15–20%. Most problematic were samples that were heterogeneous for 1RS such as the cultivar Rawhide. Incorporating heterogeneous samples into a calibration equation improved the classification accuracy of these samples but diminished the prediction accuracy of nonheterogeneous samples.