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Abstract
Alveograph Algorithms to Predict Functional Properties of Wheat in Bread and Cookie Baking. Cereal Chemistry 66:81-86
Bettge,A.D., Rubenthaler,G.L. and Pomeranz,Y.
A total of 73 wheat samples (23 soft white winter, 20 soft white spring, 15 club, seven hard red winters, six hard red springs, and two hard white winter) were milled and analyzed for gross composition and flour texture (by near-infrared reflectance spectroscopy). The wheat flours were baked into cookies and bread and evaluated on an alveograph. A multivariable model produced the highest correlation coefficient using combinations of protein, hardness and alveograph values P, L, and W to predict loaf volume, specific volume, and cookie diameter. Cookie diameter was predicted from P and protein (r=0.797; standard error [SE] = 0.14 cm). Loaf volume was predicted (r=0.914; SE = 68 cm3) in soft wheat using alveograph L and W plus protein. In hard wheat, loaf volume was predicted (r=0.950; SE = 49 cm3) using alveograph l plus protein. Specific volume (an index of protein quality) could be predicted in hard wheat (r=0.855; SE = 6.56 cm3)/% protein) using alveograph L and W plus hardness. The equations were verified using wheat flours with known gross composition and end-use parameters. Cookie diameter in soft wheat flour was predicted with r=0.934 and an average residual of 0.06 cm. Loaf volume was predicted in hard wheat flours with r=0.939 and an average residual of 33 cm3.
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