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A Single Parameter for Within‐Sample Uniformity of Seed Size in Grain, with an Emphasis on Pulses
Author(s) -
Harden Steven,
Wood Jennifer A.
Publication year - 2017
Publication title -
cereal chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.558
H-Index - 100
eISSN - 1943-3638
pISSN - 0009-0352
DOI - 10.1094/cchem-04-16-0121-r
Subject(s) - sieve (category theory) , sample size determination , statistics , sieve analysis , mathematics , selection (genetic algorithm) , particle size distribution , biological system , particle size , computer science , chemistry , biology , materials science , combinatorics , artificial intelligence , nanotechnology
The standard methodology for assessing seed size distribution of pulses is to sieve seeds into size classes, weighing each class and calculating a weighted mean of the sieve sizes (seed size index). A single unit measure of uniformity of size within a sample via sieving is not available, despite being an important trait in terms of appearance, ease of milling, and consistency in processing. This study investigated several different models for estimating the size variability within seed samples by using a variety of desi and kabuli chickpea, faba bean, lupin, lentil, and mungbean samples. Fitting a normal distribution to the frequency distribution and using the estimated variance parameter as a measure for seed size variability was found to be the most suitable method for attaining seed size and a single value of uniformity. Mean seed size ( SS norm ) and within‐sample size variability ( SV norm ) were unrelated variables, thus allowing selection of more uniform samples of any desired size in breeding programs. Sieve selection is discussed and needs to be appropriate for the samples under investigation. Examples of using the R software to calculate these measures and the R functions needed are available as supplementary files to facilitate the use of this proposed method. This method will be useful to pulse breeders and researchers and in the development of image analysis methods characterizing seed sizes of pulse samples and other grains.