Exploration of structural differences between dairy and plant-based cheese
Bibliographic record
Abstract
The functionality and microstructure of commercial dairy cheese, commercial plant-based cheese, and high-protein plant-based cheese (HPC) were analyzed to explore the structure-function relationship. Commercial dairy cheese exhibited the best functionality, showcasing an ideal texture profile alongside optimal melting and stretch characteristics. In contrast, commercial plant-based cheese showed the least resemblance to dairy cheese, with the lowest melting and stretching properties, as well as considerable structural integrity after heating, marked by the highest G’ and lowest tan δ values. HPC demonstrated notable improvements in textural properties compared to commercial plant-based cheese, indicated by enhanced melting and stretching, lower G’, and higher tan δ, reflecting its viscous nature. The microstructural analysis on cold samples revealed that both dairy and commercial plant-based cheeses contained a high density of small fat globules, while HPC had significantly larger globules and protein aggregates. Upon melting, dairy cheese and HPC displayed similar microstructures, characterized by fat pooling and a yielding continuous matrix, correlating with their melting and stretching abilities. In contrast, the melted commercial plant-based cheese exhibited minimal structural changes from its cold state, indicating that its starch matrix was irreversible and did not yield under heat. Ultimately, to achieve melting and stretching in both dairy and plant-based cheeses, the network must yield upon heating, allowing for fat pooling but maintaining connectivity of the continuous matrix. • Commercial dairy, plant-based, and high-protein cheese differ in functionality. • Synchrotron micro-computed tomography provides 3D renderings of cheese samples. • Melt and stretch of dairy and high-protein cheese link to similar microstructure. • Increased melt and stretch relate to phase separation and matrix-yielding. • Poor melt and stretch indicated matrix over-structuring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".