Evaluating the effect of plant protein functionalities on the performance of high-protein plant-based cheese
Bibliographic record
Abstract
Commercial plant-based cheese lack dairy cheese's nutritional properties and functionality. Eleven plant proteins were studied in an 18 %w/w protein and 12 %w/w waxy starch plant-based cheese to determine the relationship between protein functionality and cheese performance. Higher cheese hardness was associated with greater melt, stretch, and oil loss, with a positive correlation between tanδ and increased sample melt in kinetic melting tests. Proteins of high purity (>70 %), low solubility, low emulsion stability and high water-holding capacity result in the best cheese performance. Through FTIR spectromicroscopy and computed tomography, it was confirmed that proteins which behave as passive fillers in the cheese matrix allow the waxy starch to remain as a continuous network and have superior melt and stretch properties. Conversely, cheese that exhibited poor melting and stretch properties were associated with proteins having high solubility and/or low purity, low water-holding capacity, and high emulsifying stability causing disruption of the starch structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".