Impact of protein sources on the functionality of plant-based cheeses formulated with saturated and unsaturated fat
Bibliographic record
Abstract
Plant-based cheeses provide an environmentally friendly alternative to traditional dairy, often incorporating coconut oil to mimic the texture of animal fat. This study examines the influence of protein properties on the functionality of plant-based cheeses. Previous research has determined that a blend of 25% coconut oil (CO) and 75% sunflower oil using pea protein 1 (PP1) produces a cheese with high hardness, melt, oil loss, and stretch when compared to the same cheese made with 100% CO. This study assessed another pea protein isolate, a faba protein isolate (FP1), and a lentil protein isolate, at a concentration of 7.5% (w/w) to evaluate their effect on the cheese physical characteristics. Texture profile analysis revealed that the hardness of cheeses increased with higher amounts of coconut oil; however, the cheese formulated with PP1 and 25% CO exhibited the firmest texture of 80 N due to unique protein-fat interactions, which was similar to the hardness of 100 N at 100% coconut oil. The cheese analogs made with 25% CO and PP1 either matched or surpassed the melt, oil loss, and stretch of the cheese analogs products prepared with the same proteins with 100% CO. The rheological properties of the cheeses were assessed between 20 °C and 95 °C, using tan δ (G″/G′) and complex viscosity (η*) at 95 °C as functionality indicators. These results suggest that specific protein-fat interactions can be tuned to achieve the desired hardness while preserving functional properties and potentially improving the sustainability and health benefits of the final product.
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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 | 0.001 | 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".