Graduate Studies
First Advisor
Melanie L. Downs
Date of this Version
2017
Abstract
Soybean is a member of the regulated allergenic foods in the United States and the European Union. Allergen detection methods are important tools for industry and regulators, and mass spectrometry (MS) may offer advantages over other existing methods.
This study aimed to develop an MS-based detection and quantification method for commercially processed soy ingredients. The results of discovery analyses using MS showed that food processing reduced the content of some minor proteins such as lectin and Bowman-Birk proteinase inhibitor in some soy ingredients, while the content of the major seed storage proteins remained relatively consistent. Meanwhile, preliminary selection of target peptides for the MS method was conducted based on a set of criteria and 20 peptides were selected for further investigation. Soy proteins sourced from different soy ingredients were incurred separately in to cookies and dough to achieve final concentrations of 100, 10, and 1 ppm soy protein. The sample preparation method was optimized to obtain high peptide recovery in incurred food matrices. A substantial improvement on peptide performance was achieved when a longer extraction time and the Filter-Aided Sample Preparation technique was incorporated. Lastly, soy proteins in incurred food samples were quantified using isotopically labeled peptides. Four peptides were identified as quantitative peptides, and among which peptide VFDGELQEGR was determined to be the most sensitive peptide with the lowest quantifiable limit of 2 ppm soy proteins. All four quantitative peptides showed a near-equivalent trend of changes of abundance across all different food matrices. The quantification of soy proteins in incurred food matrices showed higher recoveries at 10 ppm compared to 100 ppm samples. The average percent recovery for baked food matrices at 10 ppm level was 74-104%, and for unbaked food matrices was 100-146%. This method provides an alternative to antibody-based methods on the detection of soy proteins sourced from different commercial soy ingredients in food matrices.
Advisor: Melanie L. Downs
Comments
A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Food Science and Technology, Under the Supervision of Professor Melanie L. Downs. Lincoln, Nebraska: December, 2017
Copyright 2017 Shimin Chen