Use of Regression and Discriminant Analyses to Develop a Quality Classification System for Hard Red Winter Wheat

Abstract

ABSTRACTAn attempt to create a segregation system that uses rapid quality detection instrumentation and represents value to millers and bakers led to the development of a single value called “dough factor.” The dough factor value represents the amount of flour‐water dough that can be produced by a given unit of wheat. Samples of hard red winter wheat (≈100/location) collected from five Kansas country elevators during the 1995 and 1996 harvests were evaluated for dough factor. Single kernel properties, sample protein content, and test weight measurements were subjected to regression and discriminant analyses for the purpose of developing a dough factor classification system. Regression analysis identified kernel weight, kernel weight standard deviation, and protein as important characteristics for predicting dough factor, however, the resulting model possessed poor predictive ability (adjusted R2 = 0.39). Classifying wheat into dough factor groups of <107, 107–112.9, and ≥113 using discriminant analysis resulted in an accuracy of 56%, while discriminant analysis correctly placed wheat into two dough factor groups (<113 and ≥113) with an 80% accuracy. Creation of a dough factor classification system using single kernel measures, kernel protein, and wheat cultivar correctly classified 86.3 and 68.8% of the wheat samples into dough factor groups <113 and ≥113, respectively. In the dough factor group ≥113, cost savings associated with higher flour yields and water absorption were $0.15/cwt of flour and $0.65/1,000 lb of dough, respectively. Increases in processing efficiency for both the miller and the baker would be expected to further differentiate the value between the two dough factor groups.