Influence of Free Fatty Acids as Additives on the Crystallization Kinetics of Cocoa Butter
Bibliographic record
Abstract
In this study, the influence of free fatty acids as additives on the crystallization kinetics of cocoa butter (CB) and the industrial tempering was considered for the first time. Different concentrations (0.5 %-5.0 %) of stearic, palmitic and oleic acid were added to CB and crystallized at isothermal temperatures between 18-28 °C. To imitate the industrial tempering, β-crystals were actively formed using a temper. The solid fat content (SFC) was determined by pulsed NMR with data fitted to the Gompertz equation and melting temperature and polymorphic behaviour were analysed by DSC. With an increase in temperature of 2-5 °C a reduction in crystallization rate of 40-80 %, lower solid fat contents and 1,5-6 times longer induction times occur. The SFC of tempered blends are significantly lower (P < 0.05 %) than their non-tempered counterparts independent of the crystallization temperature. At higher temperatures, palmitic acid and especially stearic acid act as a crystallization initiator due to their higher melting point of 63 and 69 °C, indicating shorter induction times with increasing free fatty acid content. Although pure CB crystallizes fastest, an increasing stearic acid content leads to higher crystallization rates. In contrast, palmitic and oleic acid have retarding effects. Thus, while under spontaneous conditions a temperature dependence was ascertained, after pre-crystallization the free fatty acid concentration could be identified as the driving force in terms of crystallization rate.
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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) | 0.000 | 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".