Increased accuracy of starch granule type quantification using mixture distributions

Plant Methods

Colin R. Cavanagh¹  Jean-Phillippe F. Ral²  Sean Li²  Alex Whan²  Raj Gaire²  Brian R. Cullis³  Emi Tanaka³ 

[1] Bayer CropScience Pty Ltd, Seeds;CSIRO Agriculture and Food;School of Mathematics and Applied Statistics, University of Wollongong;

关键词: Starch;    Granule type;    Mastersizer;    Mixture distribution;   

DOI  :  10.1186/s13007-017-0259-2

来源: DOAJ

【 摘 要 】

Abstract Background The proportion of granule types in wheat starch is an important characteristic that can affect its functionality. It is widely accepted that granule types are either large, disc-shaped A-type granules or small, spherical B-type granules. Additionally, there are some reports of the tiny C-type granules. The differences between these granule types are due to its carbohydrate composition and crystallinity which is highly, but not perfectly, correlated with the granule size. A majority of the studies that have considered granule types analyse them based on a size threshold rather than chemical composition. This is understandable due to the expense of separating starch into different types. While the use of a size threshold to classify granule type is a low-cost measure, this results in misclassification. We present an alternative, statistical method to quantify the proportion of granule types by a fit of the mixture distribution, along with an R package, a web based app and a video tutorial for how to use the web app to enable its straightforward application. Results Our results show that the reliability of the genotypic effects increase approximately 60% using the proportions of the A-type and B-type granule estimated by the mixture distribution over the standard size-threshold measure. Although there was a marginal drop in reliability for C-type granules. The latter is likely due to the low observed genetic variance for C-type granules. Conclusions The determination of the proportion of granule types from size-distribution is better achieved by using the mixing probabilities from the fit of the mixture distribution rather than using a size-threshold.

【 授权许可】

Unknown