B. W. Seabourn

Relationship of Bread Quality to Kernel, Flour, and Dough Properties

ABSTRACT This study measured the relationship between bread quality and 49 hard red spring (HRS) or 48 hard red winter (HRW) grain, flour, and dough quality characteristics. The estimated bread quality attributes included loaf volume, bake mix time, bake water absorption, and crumb grain score. The best-fit models for loaf volume, bake mix time, and water absorption had R2 values of 0.78–0.93 with five to eight variables. Crumb grain score was not well estimated, and had R2 values ≈0.60. For loaf volume models, grain or flour protein content was the most important parameter included. Bake water absorption was best estimated when using mixograph water absorption, and flour or grain protein content. Bake water absorption models could generally be improved by including farinograph, mixograph, or alveograph measurements. Bake mix time was estimated best when using mixograph mix time, and models could be improved by including glutenin data. When the data set was divided into calibration and prediction sets, th...

Prediction of Baking Characteristics of Hard Winter Wheat Flours Using Computer-Analyzed Mixograph Parameters

ABSTRACT The objective of this research was to determine whether computer-analyzed (objective) mixograph parameters could replace conventional mixograph parameters in the evaluation of flour quality. The 642 hard winter wheat flours, collected from federal regional performance nurseries in 1995 and 1996, were analyzed by a conventional and computerized mixograph. Mixograph bandwidths at 6 min (BW6) showed the most significant linear correlation with subjective mixing tolerance scores (r = 0.81, P < 0.1%, n = 642). Prediction models of conventional and experimental baking parameters were developed by continuum regression using computer-analyzed mixograph parameters of a calibration set (n = 282). The developed models could estimate conventional mixograph mixing time and tolerance scores, baking water absorption and mixing time, and bread loaf volume, showing R2 values of 0.86, 0.74, 0.68, 0.80, and 0.51, respectively, for a validation set (n = 380). These results indicated that computer-analyzed mixograph ...

A modified extensigraph method for evaluating dough properties of hard wheat breeding lines.

ABSTRACT A modified extensigraph method reduced sample quantity to 100 g from 300 g and testing time by half with easy dough preparation compared to the AACC standard extensigraph method, which challenges wheat breeding programs where the sample size is small and evaluations of large numbers of samples are demanded. Correlation coefficients (r) for 93 pairs of each of six extensigraph dough characteristics of 31 different tested wheat samples were r = 0.95 for resistance-to-extension, r = 0.80 for extensibility, r = 0.93 for ratio of resistance-to-extension to extensibility, r = 0.92 for ratio of maximum resistance-to-extension to extensibility, and r = 0.81 for area under the curve (energy). Correlation coefficients for the measurements of extensigraph dough characteristics at each of three rest-time tests between the modified and standard methods were significant. Some dough mixing characteristics and bake tests correlated better with dough extension characteristics when determined by the modified metho...

A Rapid Method to Determine Starch Damage in Sorghum

As a major component of cereal grains, including sorghum, starch plays an important role not only in grain development but also in post-harvest processing and end-product quality. Since milling can lead to the inadvertent disruption of starch granules, negatively affecting dough rheology, monitoring of starch damage is an important part of flour formulation. As the existing methods for quantifying starch damage are time-consuming, an alternative non-enzymatic methodology was sought. This study described the use of SDmatic—an instrument developed for determining starch damage in wheat flour—for assessment of sorghum flour via generation of a standard calibration. The known starch damage values were integrated to the SDmatic Ai%, and the starch damage was calculated using the following equation: Starch Damage = aAi%2 + bAi% + c. The following variables were developed for calibrating sorghum starch damage: a = 0.168, b = −30.123, and c = 1349.648. Using this calibration developed specifically for sorghum flour increased our linear regression from initial R2 = 0.38 to R2 = 0.95.