Evaluating the use of zein in structuring plant-based products
Bibliographic record
Abstract
The recent interest in plant-based foods has brought upon the need to develop novel structures using plant-based proteins. However, there is still room for improvement in the development of plant-based meat and cheese alternatives. The rheological properties of self-assembled zein networks were examined to evaluate potential in animal protein replacement. These plant-based protein networks were compared to gluten networks (a common ingredient in current plant-based products), chicken muscle tissue, and cheddar cheese. All samples were analyzed using temperature, amplitude, and frequency sweeps at different time points. Zein networks exhibited unique viscous behaviour (in line with that of an entangled polymer solution), in each amplitude, frequency and temperature sweeps, however only when freshly formed. The results suggest that the bonds and interactions responsible for strengthening zein networks need at least 24 h to fully form. Analysis of the secondary structure by FTIR revealed that zein undergoes a structural reorganization from intermolecular to intramolecular β-sheets during this time, but the substantial content of α-helix structures remains unchanged. Overall, different aspects of zein network rheological behaviour can be compared to either chicken breast, or cheddar cheese, presenting opportunities for zein in plant-based food structuring.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".