Determination of variation in oat kernel morphology by digital image analysis

Symons, Stephen J.; Fulcher, R. G.

doi:10.1016/s0733-5210(88)80003-1

Cited by 32 publications

(20 citation statements)

References 6 publications

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“…Correlation analysis suggested that grain size affected packing efficiency and as well as suggesting that smaller grains packed more efficiently. This is consistent with concepts forwarded by Symons and Fulcher (1988) that rounded kernels improved test weight by improving packing. The trends in packing efficiency of size fractions were largely consistent with the trends found in the unfractionated samples.…”

Section: Discussionsupporting

confidence: 88%

Oat Grain Density Measurement by Sand Displacement and Analysis of Physical Components of Test Weight

Doehlert

McMullen

2008

Cereal Chem

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Test weight or bulk density of oats (Avena sativa L.) has a major influence on the monetary value of oat grain. We hypothesize that test weight can be attributed to grain density and packing efficiency. We have measured oat grain volume and density by a sand‐displacement method and derived the packing factor for six oat cultivars grown in three environments. Volumes of individual grains were 31–38 mm3 and were highly correlated with grain mass. Grain densities were 0.96–1.03 g/cm3. Packing efficiency, defined as the space proportion occupied by the grains, was 53–55%. Regression analysis suggested that 78% of the variation in test weight could be attributed to grain density. Size fractionation of grain by sieving and analysis by digital image analysis indicated that smaller grains within an oat sample packed more efficiently than larger grains and larger grains in a sample were less dense than the smaller grains. Analysis of oat grain components indicated groat densities were ≈1.29 g/cm3 and hull densities were ≈0.69 g/cm3. The difference in densities between groat and hull provide a physical basis for the recognized relationship between groat percentage and test weight.

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Section: Discussionsupporting

confidence: 88%

Oat Grain Density Measurement by Sand Displacement and Analysis of Physical Components of Test Weight

Doehlert

McMullen

2008

Cereal Chem

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“…The β-glucan content in oats and barley varied depending on location (environmental variation) and growing seasons (Peterson et al 1995). Genetic and environmental factors can also affect other quality characteristics such as the shape and size of the groat (Symons and Fulcher 1988), which can influence the milling quality. Oat spikelets may contain one, two, three, or more kernels and the outermost of these, called the primary kernel, is the largest and mass decreases with higher orders of kernels.…”

Section: Agricultural Practicesmentioning

confidence: 99%

Factors Influencing β‐Glucan Levels and Molecular Weight in Cereal‐Based Products

2009

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Cereal Chem. 86(3):290-301The beneficial role of soluble dietary fiber in human nutrition is well documented and has lead to a growing demand for the incorporation of βglucan, particularly from oats and barley, into foods. β-Glucan with high solubility and high molecular weight distribution results in increased viscosity in the human intestine, which is desirable for increased physiological activity. Molecular weight, level, and solubility of β-glucan are affected by genotype, environment, agronomic input, and the interactions of these factors and food processing methods. Available literature reveals that the level of β-glucan in a finished product (e.g. bread, cake, muffins) depends upon several factors in the production chain, whereas food proc-essing operations are major factors affecting molecular weight and solubility of β-glucans. Therefore, to avail themselves of the natural bioactive compounds, food manufacturers must pay attention not only to ensure sufficient concentration of β-glucan in the raw material but also to the processing methods and functional properties of β-glucan, minimizing enzymatic or mechanical breakdown of the β-glucans in end-product and optimizing processing conditions. This review discusses the different sources of β-glucan for use in human functional foods and factors affecting the levels and the molecular weight of β-glucan at various pre-and postharvest operations.

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“…With digital image analysis, it is possible rapidly to measure the dimensions of individual cereal kernels (Berman et al 1996;Symons et al 1996) and thus to investigate within-sample variation for both kernel size and shape. Image analysis has been used successfully in wheat classification (Symons and Fulcher 1988b), wheat grading and cultivar identification (Draper and Keefe 1989), and in studies of variation in oat kernel morphology (Symons and Fulcher 1988a). In barley, it has been used to study differences in kernel morphology between lateral and central kernels (Gebhardt et al 1993), and to identify cultivars (Shrestha 1996).…”

Section: Introductionmentioning

confidence: 99%

QTLs affecting kernel size and shape in a two-rowed by six-rowed barley cross

Ayoub

Symons²,

Edney³

et al. 2002

Theor Appl Genet

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The suitability of barley ( Hordeum vulgare L.) grain for malting depends on many criteria, including the size, shape and uniformity of the kernels. Here, image analysis was used to measure kernel size and shape attributes (area, perimeter, length, width, F-circle and F-shape) in grain samples of 140 doubled-haploid lines from a two-rowed (cv Harrington) by six-rowed (cv Morex) barley cross. Interval mapping was used to map quantitative trait loci (QTLs) affecting the means and within-sample standard deviations of these attributes using a 107-marker genome map. Regions affecting one or more kernel size and shape traits were detected on all seven chromosomes. These included one near the vrs1 locus on chromosome 2 and one near the int-c locus on chromosome 4. Some, but not all, of the QTLs exhibited interactions with the environment and some QTLs affected the within-sample variability of kernel size and shape without affecting average kernel size and shape. When QTL analysis was conducted using data from only the two-rowed lines, the region on chromosome 2 was not detected but QTLs were detected elsewhere in the genome, including some that had not been detected in the analysis of the whole population. Analysis of only the six-rowed lines did not detect any QTLs affecting kernel size and shape attributes. QTL alleles that made kernels larger and/or rounder also tended to improve malt quality and QTL alleles that increased the variability of kernel size were associated with poor malt quality.

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Determination of variation in oat kernel morphology by digital image analysis

Cited by 32 publications

References 6 publications

Oat Grain Density Measurement by Sand Displacement and Analysis of Physical Components of Test Weight

Oat Grain Density Measurement by Sand Displacement and Analysis of Physical Components of Test Weight

Factors Influencing β‐Glucan Levels and Molecular Weight in Cereal‐Based Products

QTLs affecting kernel size and shape in a two-rowed by six-rowed barley cross

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