Insights into the mechanism of the formation of the most stable crystal polymorph of milk fat in model protein matrices
Bibliographic record
Abstract
The effect of incorporation and presence of various ingredients in a model sodium caseinate-based imitation cheese matrix on the polymorphism of milk fat was comprehensively described using powder x-ray diffraction, differential scanning calorimetry, and microscopy. With anhydrous milk fat (AMF) in bulk used as control, the embedding of AMF as droplets in a protein matrix was found to result in a greater extent of formation of the β polymorph than AMF alone and AMF homogenized with water and salts solution. The use of other protein matrices such as soy and whey protein isolate gels revealed that the nature of the protein and other factors associated with it (i.e., hydrophobicity and molecular structure) do not seem to play a role in the formation of the β polymorph. These results indicated that the most important factor in the formation of the β polymorph is the physical constraints imposed by a solid protein matrix, which forces the triacylglycerols in milk fat to arrange themselves in the most stable crystal polymorph. Characterization of the crystal structure of milk fat or fats in general within a food matrix could provide insights into the complex thermal and rheological behavior of foods with added fats.
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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".