Gluten as a unique protein building cereal product structure, is there an alternative?
Bibliographic record
Abstract
As a highly functional ingredient, gluten plays a vital structure-building role in a diversity of cereal products. However, an estimated 6% of the Canadian population is sensitive to gluten consumption and should, hence, avoid including gluten in their diet. This has led to the development of a range of food products that are gluten-free of which some focus on the use of alternative cereal proteins such as zein. The unique viscoelastic properties of gluten, however, are not that easy to replicate. The aim of the here presented work was to (i) better understand the structure of gluten in a complex dough matrix and (ii) to study the structure of zein in a protein-starch dough system. Hereto, protein structure was studied by fluorescence, FTIR and Raman spectroscopy. The results on structure were then related to dough rheology. Both gluten and zein can form a network structure upon hydration and mechanical energy input. However, the two formed networks are very different from one another in terms of their molecular and microscopic structure and the viscoelastic properties they impart to the formed dough. The insights from this study could be one of the pieces in the puzzle towards functional gluten replacement in bread-type products.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".