Crystallization Dynamics of Shear Worked Cocoa Butter
Bibliographic record
Abstract
Control of heat and mass transfer conditions during chocolate manufacture is a critical factor influencing product quality and performance. The molecular basis for specific tempering protocols developed by the chocolatiers, and used widely by the food industry, is poorly understood. Here we show that cooling and shear rates applied during cocoa butter crystallization affect the incorporation of specific triacylglycerol (TAG) molecular species onto the surface of growing seed crystals, thus affecting the different structural levels in a cocoa butter crystal network. In this work, the effects of shear work on the different structural levels in cocoa butter are determined. Results from this research show different compositions during the early stages of static crystallization at different cooling rates. In other words, there is selective attachment of TAG species onto growing crystal surfaces, leading to fractional crystallization. With the application of shear, differences between cooling rates became negligible. Shear appears to enhance the formation of mixed crystal in the growing crystals, leading to faster crystallization kinetics, the formation of a higher number of smaller crystals, and a mechanically stronger crystal network.
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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".