A Curve Fitting Method for Analyzing Starch Granule Size Distributions in Cereals

Background and Objectives: The size distribution of starch granules is an important factor in determining the functional and nutritional properties of starch. However, a simple, standardized 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. Findings: The 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. Conclusions: Curve 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 Novelty: We provide new tools and guidelines for the quantitative analysis of granule size in cereals.