Evaluation of breadmaking quality from common wheat quality parameters and fundamental rheological, chemical, and functional properties of wheat cultivars

The stress relaxation and deformability modulus of wheat doughs made from five groups of wheat cultivars of differing breadmaking ability were determined. Doughs made from poor breadmaking cultivars had greater rates of stress relaxation than doughs made from good breadmaking cultivars. Doughs made from hard wheats had a greater resistance to deformation (deformability modulus) than doughs made from soft wheats. The solubility, sulfhydryl, and disulfide content of gluten of the five groups of wheat cultivars were also determined. Gluten of poor breadmaking cultivars had greater sulfhydryl content than gluten of good breadmaking cultivars. No differences were found in the extent of stress relaxation, gluten disulfide content, and gluten solubility of the different groups of wheat cultivars. The prediction of breadmaking quality (loaf volume) by fundamental rheological, chemical, and functional properties and common wheat, flour, and dough quality tests was examined. Simple linear regression of baking tests on common wheat, flour, and dough quality tests did not result in any single test having a squared correlation coefficient greater than 0.743. General multiple regression equations predicting baking test volumes were made based on the significance of the simple linear regressions. The adjusted squared multiple correlation coefficients of these equations were as high as 0.924. A more practical prediction equation (adjusted R2=0.907) with only five independent variables (mixograph first minute slope, extensigraph extensibility to resistance ratio, sedimentaion value, thousand kernel weight, andB value) was obtained by stepwise regression analysis. The addition of the measurements of the fundamental rheological(stress relaxation, deformability modulus), chemical (sulfhydry land disulfide content), and functional (solubility) properties to the regression equations did not improve the adjusted squared multiple correlation coefficients of the equations. Principal component analysis reduced the data set to seven components which explained 91% of the total variation. Qualitative (dough strength parameters) and quantitative (milling parameters) components were identified.Land and Food Systems, Faculty ofGraduat