Molecular Composition Dynamics and Structure of Cocoa Butter
Bibliographic record
Abstract
The present study endeavors to understand how small changes in the composition of cocoa butter affect its crystal habit, crystallization behavior, microstructure, and mechanical properties. Such compositional variations were attained by blending cocoa butter with 1 and 5% 1,2,3-tristearoyl-glycerol (SSS) or 1,2,3-trilinoleoyl-glycerol (LLL). Structural parameters such as crystallization kinetics, crystal structure, microstructure, and mechanical strength were obtained via differential scanning calorimetry (DSC), pulsed nuclear magnetic resonance (pNMR), X-ray diffraction (XRD), polarized light microscopy (PLM), and texture analysis. Changes in the triacylglycerol (TAG) profile of cocoa butter affected its crystal structure and therefore its functionality. The high melting saturated SSS becomes rapidly undercooled, reducing cocoa butter’s onset crystallization times and temperatures. SSS molecules are spatially bigger (i.e., straighter) relative to cocoa butter’s symmetric monounsaturated TAG (the unsaturated oleic acid in the sn-2 position introduces a kink into the TAG structure), and hence infringe upon the order of the crystal domain, reducing the crystal−melt interfacial tension, and delaying polymorphic transformations. On the other hand, the limited molecular compatibility between cocoa butter’s TAGs and the fully unsaturated low melting LLL prevents it from co-crystallizing with the bulk of cocoa butter’s TAGs, only slightly affecting the crystallization behavior, increasing the liquid fraction, having no impact on crystal structure yet accelerating polymorphic transformations into the stable β form.
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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) | 0.002 | 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".