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United States Department of Agriculture

Agricultural Research Service

Title: Prediction of Conventional Wheat Characteristics of Hard Winter Wheats Using Single Kernel Parameters

Authors
item Chung, Okkyung
item Ohm, Jae-Bom
item Seabourn, Bradford

Submitted to: Cereal Foods World
Publication Type: Abstract Only
Publication Acceptance Date: June 18, 1999
Publication Date: November 3, 1999
Citation: Chung, O.K., Ohm, J., Seabourn, B.W. 1999. Prediction of conventional wheat characteristics of hard winter wheats using single kernel parameters. Cereal Foods World. Abstract No. 133 in: 1999 AACC Annual Meeting Program Book. p.197. Meeting Abstract.

Interpretive Summary: To be presented at the 84th AACC Meeting held October 31-November 3, 1999, in Seattle, WA.

Technical Abstract: The single kernel characteristics of 2890 hard winter wheats that were collected from federal nurseries from 1990 to 1997 were obtained from Single Kernel Characterization System (SKCS). Flour yield showed mean value of 68.8 % with a standard deviation of 2.9. Test weight (TW), % large kernels (% LK), 1000 kernel weight (KWT), and near infrared hardness score (NIR-HS) showed means values of 60 lbs/bu, 28.6 g, 67 %, and 66 with standard deviations of 2.3, 4.7, 20.0, and 12, respectively. Single kernel weight obtained from SKCS showed significant correlation coefficients (r) with KWT (r=0.923), % lLK (r=0.888), TW (r=0.521) and flour yield (r=0.384). SKCS hardness index had a significant correlation with NIR-HS (r=0.557). To develop continuum regression prediction models of TW, % LK, KWT, and flour yield, eight SKCS characteristics and 12 machine parameters were used. Among 2890 wheats, 1200 and 300 wheats were selected as calibration and validation sets, respectively. The prediction model for flour yield showed R2 of 0.643 and root mean square error (RMSE) of 1.6 for the calibration set, and R2 of 0.635 and RMSE of 1.6 for the validation set. Prediction models of KWT, % LK, NIR-HS, and TW showed R2 values of 0.898, 0.888, 0.625, and 0.472 for the calibration set and 0.893, 0.888, 0.609, and 0.473 for the validation set, respectively.

Last Modified: 10/25/2014
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