Cereal Chem. 73 (2):278-283 |
Predicting a Hardness Measurement Using the Single-Kernel Characterization System.
C. S. Gaines (1), P. F. Finney (1), L. M. Fleege (2), and L. C. Andrews (1). (1) U.S. Department of Agriculture, Agricultural Research Service, Soft Wheat Quality Laboratory, Ohio Agricultural Research and Development Center, Ohio State University, Wooster 44691. Mention of a trademark or proprietary product does not constitute a guarantee or warranty of a product by the U.S. Department of Agriculture, and does not imply its approval to the exclusion of other products that also can be suitable. (2) Department of Horticulture and Crop Science, Ohio Agricultural Research and Development Center, Ohio State University, Wooster 44691. Accepted December 28, 1995. This article is in the public domain and not copyrightable. It may be freely reprinted with customary crediting of the source. American Association of Cereal Chemists, Inc., 1996.
The single-kernel characterization system (SKCS) crushes individual kernels and uses algorithms based on the force-deformation profile data to classify wheat samples into soft, hard, or mixed market classes. Those data were utilized to produce a predictive equation for softness equivalent (SE), a direct measure of wheat kernel texture obtained from milling wheat on a modified Brabender Quadrumat Jr. mill and sieving system. Predicted SE values had a high correlation (r(^2) = 0.996) with actual SE milling values. In contrast to SKCS hardness index values, predicted SE values accurately responded to varying kernel moisture content and kernel size, within the ranges examined. Therefore, using the SKCS data to predict an independent measure of kernel texture (e.g., SE) may be a valuable augmentation to or replacement for using SKCS algorithms to classify wheat.