Increased accuracy of starch granule type quantification using mixture distributions

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.

Tags
Data and Resources
To access the resources you must log in

This item has no data

Identity

Description: The Identity category includes attributes that support the identification of the resource.

Field Value
PID https://www.doi.org/10.6084/m9.figshare.c.3947113
PID https://www.doi.org/10.6084/m9.figshare.c.3947113.v1
URL http://dx.doi.org/10.6084/m9.figshare.c.3947113
URL http://dx.doi.org/10.6084/m9.figshare.c.3947113.v1
Access Modality

Description: The Access Modality category includes attributes that report the modality of exploitation of the resource.

Field Value
Access Right not available
Attribution

Description: Authorships and contributors

Field Value
Author Tanaka, Emi
Author Jean-Phillippe Ral
Author Li, Sean
Author Gaire, Raj
Author Cavanagh, Colin
Author Cullis, Brian
Author Whan, Alex
Publishing

Description: Attributes about the publishing venue (e.g. journal) and deposit location (e.g. repository)

Field Value
Collected From Datacite
Hosted By figshare
Publication Date 2017-01-01
Publisher Figshare
Additional Info
Field Value
Language UNKNOWN
Resource Type Collection
keyword FOS: Biological sciences
system:type other
Management Info
Field Value
Source https://science-innovation-policy.openaire.eu/search/other?orpId=dedup_wf_001::3ebf36c9bc733365d2eb0a8da60debf3
Author jsonws_user
Last Updated 19 December 2020, 23:50 (CET)
Created 19 December 2020, 23:50 (CET)