Relationship of bread quality to kernel, flour, and dough properties

• Published in: Cereal chemistry Vol. 85; no. 1; pp. 82 - 91
• Main Authors: Dowell, F.E, Maghirang, E.B, Pierce, R.O, Lookhart, G.L, Bean, S.R, Xie, F, Caley, M.S, Wilson, J.D, Seabourn, B.W, Ram, M.S
• Format: Journal Article
• Language: English
• Published: St. Paul, MN The American Association of Cereal Chemists, Inc 2008; American Association of Cereal Chemists
• Subjects: absorption; baking; Biological and medical sciences; bread dough; breadmaking quality; breads; calibration; Cereal and baking product industries; diameter; Food industries; food quality; Fundamental and applied biological sciences. Psychology; hard red spring wheat; hard red winter wheat; loaves; mathematical models; protein content; simulation models; test weight; texture; volume; water; water content; wheat flour; Bakery product; Dough; Flour; Cereal product; Quality; Bread
• ISSN: 0009-0352; 1943-3638
• DOI: 10.1094/CCHEM-85-1-0082

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 [almost equal to]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, bulk moisture content, and dark hard and vitreous kernels), only loaf volume could be predicted with accuracies adequate for screening samples.