Wheat gluten: A functional protein still challenging to replace in gluten‐free cereal‐based foods
Bibliographic record
Abstract
Abstract Background and Objectives Wheat gluten in cereal‐based products has unique functionality derived from its viscoelastic properties. Nevertheless, many food applications require its replacement to obtain gluten‐free foods but keeping similar quality characteristics. This review analyzes the distinctive characteristics of wheat gluten, and the technological strategies implemented to mimic its behavior within the gluten‐free systems. Findings The viscoelastic behavior of wheat gluten is due to the interplay of glutenins and gliadins after being hydrated and subjected to mechanical stress. Disulfide bonds and noncovalent interactions are key in holding its structure and explaining its solubility and hydrophobicity. Gluten‐free flours and starches have represented the first adopted strategies for gluten replacement, but results have not been completely satisfactory. To tackle this issue, non‐wheat protein addition, physical treatments, hydrocolloids, enzymes, and emulsifiers have allowed recreation of a pseudo gluten network of the cereal‐based foods. Conclusions Despite technological sensorial achievements, a gap still exists when gluten‐free products are compared with their wheat‐based counterparts. A better comprehension about the combined actions of different processing aids and technologies could offer future answers. Significance and Novelty The review points out the main characteristics of the wheat gluten uniqueness, shedding light on its replacement strategies to guide future research.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".