A curve fitting method for analysing starch granule size distributions in cereals

Background and ObjectivesThe size distribution of starch granules is an important factor determining functional and nutritional properties of starch. However, a simple, standardised method for their analysis is lacking. Here, we developed an approach for estimating granule size parameters using a Python script that fits curves to volumetric granule size distributions generated using a Coulter counter. FindingsThe bimodal size distribution of starch from most wheat and barley cultivars could be best described with a mixed distribution curve. A log-normal distribution was fitted to the small B-type granules, and a normal distribution was fitted to the large A-type granules, allowing estimation of their relative abundance and size parameters, despite their overlapping size distributions. However, the optimal fitting is altered in wheat mutants with large perturbations in B-type granule content. In maize and rice, which have unimodal granule size distributions, size parameters were calculated by fitting a single normal distribution. ConclusionsCurve fitting is an effective approach for estimating starch granule size parameters in diverse cereals, particularly the Triticeae with A- and B-type granules. Significance and noveltyWe provide new tools and guidelines for the quantitative analysis of granule size in cereals.