Submitted to: Crop Science
Publication Type: Peer reviewed journal
Publication Acceptance Date: 11/14/2003
Publication Date: 6/1/2004
Citation: Doehlert, D.C., Mcmullen, M.S., Jannink, J., Panigrahi, S., Gu, H., Riveland, N. 2004. Evaluation of oat kernel size uniformity. Crop Science. Vol. 44:1178-1186. Interpretive Summary: The uniformity of oat kernel size is important to the oat milling industry because the oat mill will separate oats according to kernel size prior to dehulling and flaking. Greater uniformity of kernel size distributions allows for a more efficient milling process. We analyzed oats size uniformity by two methods, sequential sieving and digital image analysis. In sequential sieving, oats were passed through a series of slotted sieves of decreasing size. Uniformity was evaluated from the mass proportions of grain held back by certain sized sieves. In digital image analysis, a digital photograph of oat kernels laying on a light background is analyzed by a computer to determine the length and width of each kernel in the photograph. This method allows for the accurate measurement of thousands of oat kernels in a short time. Both methods provided information about the oat quality. Digital image analyses indicated that most oat size distributions had two distinct peaks, as if there were two subpopulations of oats in every sample, one being larger, the other smaller. These probably correspond to the primary and secondary kernels of the double kernel oat spikelet. A mathematical model was developed that provided the mean kernel size for each of these populations, and allows for statistical analysis, even though the distributions are not normal. Sequential sieving also provided good estimations of oat kernel size uniformity, and because it is much easier to use, it is the most likely tool to be used by plant breeders interested in selecting for kernel size uniformity.
Technical Abstract: Oat kernel size uniformity is important to the oat milling industry because oat-processing mills separate oats into different size streams to optimize dehulling efficiency. In this study, we compared two different approaches for analyzing oat kernel size uniformity, namely the sequential sieving of oat samples with a gradient of slotted sieve sizes and digital image analysis. Histograms of oat kernel sizes derived from digital image analysis suggested oat kernel sizes were (within a genotype and location) composed of bimodal populations. A new statistical analysis allowed for the derivation of means and variances of each of these subpopulations. Sequential sieving did not generate multimodal distributions, but also afforded much less resolution. Image analysis of size fractions provided evidence that sieving separated oat kernels according to their depth, whereas, digital image analysis measured kernel length and width, and derived a measure of the area of the oat kernel image. Both methods appear satisfactory for evaluating oat kernel size uniformity, although the sequential sieving method is likely to be more useful to breeding programs because of its relative technical ease and simplicity.