High-Moisture Shear Processes: Molecular Changes of Wheat Gluten and Potential Plant-Based Proteins for Its Replacement
Bibliographic record
Abstract
Nowadays, a growing offering of plant-based meat alternatives is available in the food market. Technologically, these products are produced through high-moisture shear technology. Process settings and material composition have a significant impact on the physicochemical characteristics of the final products. Throughout the process, the unfolded protein chains may be reduced, or associate in larger structures, creating rearrangement and cross-linking during the cooling stage. Generally, soy and pea proteins are the most used ingredients in plant-based meat analogues. Nevertheless, these proteins have shown poorer results with respect to the typical fibrousness and juiciness found in real meat. To address this limitation, wheat gluten is often incorporated into the formulations. This literature review highlights the key role of wheat gluten in creating products with higher anisotropy. The generation of new disulfide bonds after the addition of wheat gluten is critical to achieve the sought-after fibrous texture, whereas its incompatibility with the other protein phase present in the system is critical for the structuring process. However, allergenicity problems related to wheat gluten require alternatives, hence an evaluation of underutilized plant-based proteins has been carried out to identify those that potentially can imitate wheat gluten behavior during high-moisture shear processing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".