Wheat kernel hardness is a measure of the kernel texture and an important
indication of baking qualities of flour produced from the wheat.
While wheat can have a broad range of hardness values, there are two main
categories or classes, of wheat based on hardness – soft and hard.
It is desirable to market and trade wheat of a pure hardness class as
it will have more predictable end use qualities.
One of the most commonly used methods for measuring wheat hardness and
determining purity of hardness classes in loads of wheat is the Single
Kernel Characteristic System (SKCS).
However, for some varieties of wheat, particularly those grown in the
Pacific Northwest, the SKCS has trouble distinguishing kernels from hard and soft classes.
This leads to errors in determining if a sample is pure hard wheat, pure
soft wheat, or a mixture.
This research focused on improving the accuracy of the SKCS for wheat
grown in the Pacific Northwestby use of more modern digital signal processing of the data that the SKCS
already produces and by combining images with the SKCS.
It was found that integrating new signal processing techniques into the
SKCS software can reduce in half the errors made by the SKCS.
By adding data extracted from images of kernels, the errors can be reduced
by more than 70%.
This technology should help wheat inspectors to determine the proper quality
of a load of wheat, expecially at export terminals.
This will help improve the quality and international competitiveness of
wheat produced in the United States.
Reprinted from GMPRC Research Kernels, February 2008 issue.
For more information contact:
Dr. Tom Pearsonat