|Seabourn, Bradford - Brad|
|Park, Seok Ho|
Submitted to: Cereal Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/4/2007
Publication Date: 1/1/2008
Publication URL: http://dx.doi.org/10.1094/CCHEM-85-1-0082
Citation: Dowell, F.E., Maghirang, E.B., Pierce, R.0., Lookhart, G.L., Bean, S., Xie, F., Caley, M.S., Wilson, J.D., Seabourn, B.W., Ram, M.S., Park, S., Chung, O.K. 2008. The Relationship of Bread Quality to Kernel, Flour, and Dough Properties. Cereal Chemistry. 85(1):82-91. Online. doi: 10.1094/CCHEM-85-1-0082. Available http://cerealchemistry.aaccnet.org/toc/cchem/85/1. Interpretive Summary: It is difficult to examine wheat kernels, or the flour or dough from those kernels, and determine if they can be used to make a good loaf of bread. However, breeders need to know if their breeding lines will bake well, and millers and bakers need to know if grain or flour they buy will result in good quality bread. We worked with the Federal Grain Inspection Service to select commercial samples on which we measured about 50 different grain, flour, and dough attributes. We then developed models to predict bread quality including loaf volume, bake mix time, and water absorption. Resulting models showed that these quality indicators could be predicted with accuracies sufficient for screening samples. These results will help breeders develop lines with good bread quality, and help millers and bakers adjust their processes to maximize profits and give domestic and international consumers a consistently high-quality product.
Technical 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 to 0.93 with five to eight variables. Crumb grain score was not well estimated, and had R2 values around 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, the loaf volume and bake mix time models still looked promising for screening samples. When including only variables that could be rapidly measured (protein content, test weight, single kernel moisture content, single kernel diameter, single kernel hardness, and bulk moisture content, and dark hard and vitreous kernels), only loaf volume could be predicted with accuracies adequate for screening samples.