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United States Department of Agriculture

Agricultural Research Service

1 - Modeling Erosion of Particulate Matter
2 - Micro-Quality: Every Kernel Counts
3 - Lincoln company develops new weapon for the weevil wars
4 - Chilly reception runs off unwanted bugs!
5 - ARS, Industry Cooperation Yields Device to Detect Insects in Stored Wheat
6 - Monitoring mold by measuring CO2
7 - Sorter Detects and Removes Damaged Popcorn Kernels
8 - ARS Scientist Wins The Andersons Research Grant Program: Team Competition
9 - How Far Does Dust Travel During a Wind Erosion Event?
10 - Non-Destructive Prediction of Protein, Starch, & Moisture using NIR Spectroscopy
11 - SKCS technology Increases Accuracy Identifying Soft & Hard Wheat Grown in Pacific Northwest
12 - From Granaries to Insectaries: NIR Technology Helps Human Health
13 - Insects Play Hide and Seek in Wheat
14 - Near-Infrared Spectroscopy Detects Honey Bee Queen Insemination
15 - Sensor offers a Promising Means to Determine the Moisture Content of Grain During Storage or Transportation in Cargo Holds
16 - Pulsewave™ Technology Reduces Grain to Flour at Lower Energy Costs
SKCS technology Increases Accuracy Identifying Soft & Hard Wheat Grown in Pacific Northwest

 

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 thomas.pearson@ars.usda.gov

 
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ARS News Articles

Modeling Erosion of Particulate Matter
Aug 05, 2011
ARS, Industry Cooperation Yields Device to Detect Insects in Stored Wheat=
Jun 24, 2010
Norman Borlaug Fellow Presents Results in Costa Rica
Feb 08, 2006
Last Modified: 8/8/2011