U.S. flag

An official website of the United States government

Dot gov

Official websites use .gov
A .gov website belongs to an official government organization in the United States.


Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.


Main content area

Modeling End-Use Quality in U.S. Soft Wheat Germplasm

Alecia M. Kiszonas, E. Patrick Fuerst, Craig F. Morris
Cereal chemistry journal 2015 v.92 no.1 pp. 57-64
Triticum aestivum, arabinoxylan, ash content, baking quality, catechol oxidase, cookies, flour, germplasm, grain quality, hardness, lactic acid, milling, models, prediction, protein content, regression analysis, seeds, sodium carbonate, sucrose, varieties, viscosity, wheat, wheat protein, United States
End-use quality in soft wheat (Triticum aestivum L.) can be assessed by a wide array of measurements, generally categorized into grain, milling, and baking characteristics. Samples were obtained from four U.S. regional nurseries. Selected parameters included test weight, kernel hardness, kernel size, kernel diameter, wheat protein, polyphenol oxidase activity, flour yield, break flour yield, flour ash content, milling score, flour protein content, flour SDS sedimentation volume, flour swelling volume, Rapid Visco Analyzer peak paste viscosity, solvent retention capacity (SRC) parameters, total and water-extractable arabinoxylan (TAX and WEAX, respectively), and cookie diameter. The objectives were to model cookie diameter and lactic acid SRC as well as to compare exceptionally performing varieties for each quality parameter. Cookie diameter and lactic acid SRC were modeled by using multiple regression analyses and all of the aforementioned quality parameters. Cookie diameter was positively associated with peak paste viscosity and was negatively associated with or modeled by kernel hardness, flour protein content, sodium carbonate SRC, lactic acid SRC, and water SRC. Lactic acid SRC was positively modeled by break flour yield, milling score, flour SDS sedimentation volume, and sucrose SRC and was negatively modeled by flour protein content. Exceptionally high- and low-performing varieties were selected on the basis of their responses to the aforementioned characteristics in each nursery. High- and low-performing varieties exhibited notably wide variation in kernel hardness, break flour yield, milling score, sodium carbonate SRC, sucrose SRC, water SRC, TAX content, and cookie diameter. This high level of variation in variety performance can facilitate selection for improved quality based on exceptional performance in one or more of these traits. The models described allow a more focused approach toward predicting soft wheat quality.