Determining the lmpact of Genotype × Environment on oat protein isolate structure, function, and composition
Bibliographic record
Abstract
Oat has recently received widespread attention owing to its relatively high protein content (15-20%) among cereals. Given the increased use of plant proteins in the food industry, oat protein isolate is also considered a sustainable plant protein alternative. The characteristics of oat protein depend on both genotype and the environment in which it is grown. Therefore, the genotype × environment interaction (G × E) has a major influence on protein composition, structure, and functionality which is of great importance in the context of end product quality and consistency. The goal of the first study of the thesis was to evaluate the effect of G × E on oat protein structural characteristics and functionality using three oat genotypes (Summit, AC Morgan, and CS Camden) grown in three different environments (Manitoba, Alberta, and Saskatchewan) in the Canadian Prairies. The second study aimed at determining the effect of G × E on the relative protein composition through SE-HPLC and the use of LC-MS for protein identification. Oat protein isolate (OPI) was extracted from defatted oat flour at pH 9 at a 1:5 flour:NaOH ratio and precipitated at pH 5.3. The OPI samples were freeze-dried and evaluated for structural characteristics such as protein profile, surface hydrophobicity, secondary structure, and denaturation characteristics, as well as functional properties such as solubility, foaming, emulsification, and gelling. All structural and functional tests were conducted at pH 7. The first study indicated that oat protein content, protein profile, and functional properties are dependent on genotype and the environment. The surface hydrophobicity indicated a stronger impact from the growing environment and samples from Alberta showed the highest surface hydrophobicity. The denaturation temperature for OPI ranged from 110.2 -111.6 °C. The water solubility of OPI was significantly impacted by G × E, where the solubility ranged from 13-30 %. Samples Alberta – Summit exhibited the highest foaming capacity, while all samples tested had good foaming stability >70%. Furthermore, it was found that G×E significantly impacts OPI structure and functionality including denaturation enthalpy, protein solubility, foaming capacity, emulsion stability, and textural characteristics. The SE-HPLC separated the OPI into four major fractions including polymeric globulin, avenins, glutelins and albumins as well as smaller peptides, whereas LC-MS was able to identify eight major types of proteins present in OPI including globulins, prolamins/avenins, glutelins, enzymes/ albumins, enzyme inhibitors, heat shock proteins, grain softness proteins and allergenic proteins. The SE-HPLC analysis revealed that environment has a stronger effect on overall oat protein composition, while LC-MS results indicated a significant genotypic effect on the main globulin protein type in OPI. PCA results indicated that certain environment and genotypic combinations could be better choices in terms of globulin protein quality given their positive and negative associations with certain genotypes and environments. Overall, the results indicate that genotype, environment and G×E significantly impact OPI structure, functionality and composition, highlighting the need for future work to further expand the examination of OPI structure-function and compositional characteristics to determine oat cultivars that can be used to produce OPI of consistent quality. To that end, this study provides new insights into the selection of oat genotypes and environment combinations for targeted protein applications in the food industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".