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ARS Home » Research » Publications at this Location » Publication #103894


item Delwiche, Stephen - Steve
item Graybosch, Robert

Submitted to: ASAE Annual International Meeting
Publication Type: Proceedings
Publication Acceptance Date: 7/18/1999
Publication Date: N/A
Citation: N/A

Interpretive Summary: Starch in plants is synthesized within specialized organelles called amyloplasts. The two macromolecules that comprise starch, namely amylose and amylopectin, are chemically similar, but differ in the degree of branching of the core building units. It is this difference that causes very-low-amylose (i.e., waxy) starch to have unique cooking and processing characteristics compared to normal starch. Potential uses of waxy or low-amylose (partial waxy) starches, particularly those derived from wheat, include the production of Asian noodles, paper, and adhesives. To eventually identify waxy and partial waxy wheats in the market stream, a rapid and reliable test will be needed. The current study has examined the feasibility of near-infrared (NIR) spectroscopy as a basis for classifying wheat into normal vs. partial waxy vs. waxy categories. NIR spectroscopy is already used extensively in the grain industry for quantitative and qualitative assessment of quality; therefore, adaptation of this technique to starch categorization would likely be accepted by the industry. On a limited number (ca. 200) of breeders samples, we have determined that NIR is highly accurate (nearly 100%) for the identification of waxy wheats. The accuracy level drops when distinguishing partial waxy wheats, though at 80%, it is still probably useful in trade and breeding programs. This report summarizes the findings of year one of a two-year study. The second year will be devoted to model verification and identifying the spectral basis of the NIR classification models. Traders and millers are the intended beneficiaries of the development of an NIR method for waxy and partial waxy wheat identification.

Technical Abstract: Newly proposed low amylose wheat varieties, currently under development, will have unique processing characteristics, and thus allow millers to blend defined levels of amylose in mixes requiring low-amylose flour. The amount of amylose synthesized during grain fill is dependent on the expression of three structural genes that encode isoforms of granule-bound starch synthase (GBSS). Lines possessing all three waxy loci as fully functional produce the highest proportion of amylose to amylopectin, while those with one or more null alleles produce successively smaller proportions. The present study was undertaken to determine the feasibility of using NIR spectroscopy to classify wheat by the number of active GBSS genes. Nearly 200 lines of 1998-harvest wheat with 0-3 active genes were scanned (1100-2500 nm) in NIR reflectance. Classification algorithms, such as SIMCA (Soft Independent Modeling of Class Analogy) on PCA (Principal Component Analysis) with a Mahalanobis distance (MD) metric, PCA of a one-population space, and PLS (Partial Least Squares) regression (0 = no active genes,..., 3 = all genes active), demonstrated that perfect separation of fully waxy lines was achievable. The SIMCA PCA/MD algorithm was most effective at separating intermediate cases (>80% accuracy). Misclassifications were most often assignments into neighboring gene classes (e.g., 1-gene line assigned to the 2-gene class). Possible reasons for the success of NIR modeling include a spectral sensitivity to amylose vs. amylopectin (likely), a spectrally sensed dependency of particle size distribution on the number of active genes (possibly), and a spectral sensitivity to the concentration of GBSS (least likely).