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.