Effects of Cocoa Butter Origin, Tempering Procedure, and Structure on Oil Migration Kinetics
Bibliographic record
Abstract
The effects of cocoa butter (CB) origin, tempering procedure, and structure on oil migration kinetics were studied using a flatbed scanner followed by image processing and analysis. The migration rate (OMR) and migration distance ( I 10 ) of stained oil were determined in tempered and nontempered CBs. Tempered matrices had 10 to 50 times lower OMRs than nontempered CBs. In addition, the lag phase observed before significant oil migration was also significantly longer in tempered CBs (12 days vs 2 days in nontempered butters). Moreover, cocoa butter origin had a strong effect on OMR. Brazilian and Nigerian CBs had the highest OMR in both tempered and nontempered samples. Malaysian CB had the third highest OMR, but this effect was only significant in untempered samples. Finally, the lowest OMRs were found in Chinese, Ecuadorian, and Ivorian cocoa butters. The amount of oleic acid and triunsaturated triglycerides (UUU) was strongly correlated to OMR (the higher the UUU concentration, the lower the resistance to oil migration). However, the relationships between the permeability coefficients and structural factors (squared averaged particle size and crystalline domain size) suggest that the micro- and nanostructure of the material also plays a significant role in the oil migration process.
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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 | 0.002 |
| 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) | 0.001 | 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".