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

¹USDA ARS, Grain Marketing and Production Research Center, Engineering Research Unit, 1515 College Avenue, Manhattan, KS 66502. Mention of trade names or commercial products is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. ²Corresponding author. Phone: 785-776-2753. Fax: 785-537-5550. E-mail address: floyd.dowell@gmprc.ksu.edu ³USDA, Grain Inspection, Packers, and Stockyards Administration, Federal Grain Inspection Service, Kansas City, MO 64163. ⁴Kansas State University, Dept. Grain Science and Industry, Manhattan, KS 66506. ⁵USDA ARS, Grain Marketing and Production Research Center, Grain Quality and Structure Research Unit, 1515 College Avenue, Manhattan, KS 66502.

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, 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.