Cereal Chem 66:81-86   |  VIEW ARTICLE

Alveograph Algorithms to Predict Functional Properties of Wheat in Bread and Cookie Baking.

A. Bettge, G. L. Rubenthaler, and Y. Pomeranz. Copyright 1989 by the American Association of Cereal Chemists, Inc. 

A total of 73 wheat samples (23 soft white winter, 20 soft white spring, 15 club, seven hard red winter, six hard red spring, 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 wheats using alveograph L and W plus protein. In hard wheats, 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 wheats (r = 0.946; SE = 3.1 cm3/% protein) using alveograph P, W, and hardness. In soft wheats, specific volume was predicted (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.