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ARS Home » Research » Publications at this Location » Publication #63895


item Gaines, Charles
item Finney, Patrick
item Andrews, Lonnie

Submitted to: Cereal Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/30/1995
Publication Date: N/A
Citation: N/A

Interpretive Summary: The new Single Kernel Characterization System (SKCS) (Perten Instruments, North America, Reno, NV) produces data based on the crushing of wheat kernels that are used to assign wheat samples into soft, hard, or mixed class categories. In addition to the classification, the main values given the operator are mean values for kernel hardness, size, weight, and moisture. The system is based on the conversion of crushing data to an arbitrary mathematical scale and not on a direct measurement of kernel texture. As such, it is not necessarily as reliable as it could be. The SKCS data was adversely affected by large differences in kernel moisture content and kernels having relative small size. We used the SKCS data to predict a direct measurement of kernel hardness that could make wheat classification more reliable. The predicted measurements were softness equivalent (SE). SE was not adversely influenced by moisture or size. Predicting SE values with the SKCS will decrease the errors in classification that may result from evaluating relatively wet or small kerneled wheat. The findings will also assist the manufacturer of the SKCS to expand the design, scope, and application of the instrument.

Technical Abstract: The single kernel characterization system classifies wheat into soft, hard, or mixed classes. Data generated by the instrument were utilized to produce a predictive equation for softness equivalent, a direct measurement of wheat kernel texture obtained from milling wheat on a Brabender Quadrumat Jr. mill and sieving system. Predicted softness equivalent values had a high correlation with actual milling values. In contrast to single kernel characterization system hardness index values, predicted softness equivalent values were not adversely influenced by kernel moisture content or kernel size. Therefore, the technique of predicting an independent measurement of kernel texture may be a valuable augmentation to, or may be equivalent, if not better than, only using algorithms based on instrument crushing of kernels to classify wheat.