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Single Kernel Near-Infrared Analysis of Tetraploid (Durum) Wheat for Classification of the Waxy Condition

¹USDA-ARS, Beltsville Agricultural Research Center, 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. Phone: 301-504-8450. E-mail: delwiche@ba.ars.usda.gov ³USDA-ARS, Department of Agronomy, University of Nebraska, Lincoln, NE. ⁴University of Idaho, Plant Breeding and Genetics Department, Aberdeen Research and Extension Center, Aberdeen, ID. ⁵USDA-ARS, Grain Marketing and Production Research Center, Manhattan, KS.

Plant breeding programs are active worldwide in the development of waxy hexaploid (Triticum aestivum L.) and tetraploid (T. turgidum L. var. durum) wheats. Conventional breeding practices will produce waxy cultivars adapted to their intended geographical region that confer unique end use characteristics. Essential to waxy wheat development, a means to rapidly and, ideally, nondestructively identify the waxy condition is needed for point-of-sale use. The study described herein evaluated the effectiveness of near-infrared (NIR) reflectance single-kernel spectroscopy for classification of durum wheat into its four possible waxy alleles: wild type, waxy, and the two intermediate states in which a null allele occurs at either of the two homologous genes (Wx-1A and Wx-1B) that encodes for the production of the enzyme granule bound starch synthase (GBSS) that controls amylose synthesis. Two years of breeders' samples (2003 and 2004), corresponding to 47 unique lines subdivided about equally into the four GBSS genotypes, were scanned in reflectance (1,000–1,700 nm) on an individual kernel basis. Linear discriminant analysis models were developed using the best set of four wavelengths, best four wavelength differences, and best four principal components. Each model consistently demonstrated the high ability (typically >95% of the time) to classify the fully waxy genotype. However, correct classification among the three other genotypes (wild type, wx-A1 null, and wx-B1 null) was generally not possible.