The effect of minor components on milk fat crystallization
Bibliographic record
Abstract
Abstract Milk fat is composed of 97–98% triacylglycerols and 2–3% minor polar lipids. In this study triacylglycerols were chromatographically separated from minor components. Isolated diacylglycerols from the polar fraction were also added back to the milk fat triacylglycerols. The crystallization behaviors of native anhydrous milk fat (AMF), milk fat triacylglycerols (MF‐TAG), and milk fat triacylglycerols with diacylglycerols added back (MF‐DAG) were studied. Removal of minor components and addition of diacylglycerols had no effect on dropping points or equilibrium solid fat contents. Presence of the minor components, however, did delay the onset of crystallization at low degrees of supercooling. Crystallization kinetics were quantified using the Avrami model. Sharp changes in the values of the Avrami constant k and exponent n were observed for all three fats around 20.0°C. Increases in n around 20.0°C indicated a change from one‐dimensional to multidimensional growth. Differences in k and n of MF‐DAG from AMF and MF‐TAG suggested that the presence of milk fat diacylglycerols changes the crystal growth mechanism. Apparent free energies of nucleation (ΔG c,apparent ) were determined using the Fisher‐Turnbull model. (ΔG c,apparent ) for AMF was significantly greater than ΔG c,apparent for MF‐TAG, and ΔG c,apparent for MF‐DAG was significantly less than those for both AMF and MF‐TAG. The microstructural networks of AMF, MF‐TAG, and MF‐DAG, however, were similar at both 5.0 and 25.0°C, and all three fats crystallized into the typical β′‐2 polymorph. Differential scanning calorimetry in both the crystallization and melting modes revealed no differences between the heat flow properties of AMF, MF‐TAG, and MF‐DAG.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".