Seeding and gelation properties of lentil protein nanofibrils
Bibliographic record
Abstract
Converting plant proteins into functional amyloid fibrils may be an effective means to improve their utility in novel foods and materials. Most research on engineered fibrils has focused on animal proteins, while plant seed storage proteins tend to be more heterogeneous and poorly characterized regarding their fibril assembly into films, gels, and hierarchical structures. This work studied the kinetics of lentil protein fibrillization, the conversion yield, the effect of seeding, and the gelation properties of crude and isolated fibrils. Seeding with 3% (w/w) preformed fibril fragments significantly decreased the lag time (from 6.5 h to 3.8 h) (p<0.05), whereas the growth rate, conversion yield, and morphology were not affected significantly by seeding. Lentil protein fibrils were able to form colorless, translucent thermal gels after heating the protein extract at pH 2 for 24 h. The gel network formed by the fibrils was fine and compact, providing superior mechanical strength (5.2 ± 0.7 kPa) and water holding capacity (87%) to the gel at a relatively low protein concentration (6%). Isolated fibrils were susceptible to post-fibrillization processing, including vacuum evaporation and increasing the pH from 2 to 3–8, altering the fibril morphology and reducing thermal and cold gelation functionality.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".