Submitted to: Cereal Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/19/2007
Publication Date: 11/1/2007
Publication URL: http://naldc.nal.usda.gov/download/15928/PDF
Citation: Pearson, T.C., Wilson, J.D., Gwirtz, J., Maghirang, E.B., Dowell, F.E., Mccluskey, P., Bean, S. 2007. The Relationship Between Single Wheat Kernel Particle Size Distribution and the Perten SKCS 4100 Hardness Index. Cereal Chemistry. 84(6):567-575. Online. doi:10.1094/CCHEM-84-6-0567. Interpretive Summary: Grain inspectors have observed that in the U.S. Pacific Northwest (PNW) region, discriminating soft white wheat from hard white wheat has become increasingly difficult. This poses problems for assigning a proper grade to wheat loads being exported to international customers. Additionally, wheat loads with mixed hard white and soft white wheat may have different baking qualities, and their presence reduces the desirability of U.S. grown wheat, especially for international customers. The primary instrument for distinguishing hard and soft classes of wheat is called the Single Kernel Characterization System (SKCS). This research found that, through simple data processing software changes to the SKCS, classification errors between hard white and soft white wheat can be reduced by about 50% over the current configuration of the SKCS. This should help those who use the SKCS to determine wheat class purity, such as wheat inspectors. Also, this should help wheat millers and international customers better understand the quality and properties of incoming wheat loads.
Technical Abstract: The Perten Single Kernel Characterization System (SKCS) is the current reference method to determine single wheat kernel texture. However, the SKCS calibration method is based on bulk samples, and there is no method to determine the measurement error on single kernel hardness. The objective of this research was to develop a single-kernel hardness reference based on single-kernel particle size distributions (PSD). A total of 473 kernels drawn from eight different classes were studied. Material from single kernels crushed on the SKCS was collected and milled in a fabricated mill, which simulates the last two rolls of a Quadrumat Jr. The PSD of each single kernel was then measured using a laser particle counter. Calibrations using data from the PSD and SKCS were then used to estimate single kernel PSD and classify kernels into their genetic classes. Wheat kernels from soft and hard classes having SKCS hardness indices (HI) between 40 and 60 typically had a PSD that is expected from their genetic class, even though their HI overlapped. That is, soft kernels tend to have more particles below 21 micrometers than hard kernels do after milling. As such, a combination of HI and PSD gives better discrimination between genetically hard and soft classes than either parameter measured independently. Additionally, use of SKCS predicted PSD combined with other low level SKCS parameters appears to reduce classification errors into genetic hardness classes by about 50% over what can currently be accomplished with HI alone.