Developing an Analytical Tool to Classify Wheat Gluten Protein Quality for Breeding Programs

This project is in partnership with AFMNet. There are a number of common techniques used to evaluate wheat quality for food use in breeding programs, however they require large samples sizes (100 – 500 grams). Therefore, samples can only be evaluated at later stages of crossing (4th or 5th generation) when enough sample is available for testing. The proposed research will develop a technique to classify gluten protein quality at earlier stages of breeding with sample sizes as small as 20 grams. The tool that will be used for the study is known as a Gluten Peak Tester (GPT). Although there is evidence for the potential of this machine to evaluate gluten protein quality parameters, more work must be completed to determine optimal parameters for testing gluten protein quality in wheat. The objectives of the study are to: 1. Define the measurement parameters and settings in the GPT to measure gluten properties; variables to be tested include water to flour ratio, temperature of measurement, and the rpm of the spindle 2. Validate the optimal settings using different control wheat varieties with known properties and functionality 3. Evaluate gluten protein quality on early generation breeding lines The partner company, Hyland Seeds, will benefit from the internship because they will be able to screen for protein quality in earlier stages of breeding using smaller sample sizes. This will allow them to retain seeds that possess unique protein quality attributes for future development work.

John Melnyk
Faculty Supervisor: 
Dr. Koushik Seetharaman