Algal Butter, a Novel Cocoa Butter Equivalent: Chemical Composition, Physical Properties, and Functionality in Chocolate
Bibliographic record
Abstract
Abstract Cocoa butter equivalents (CBE) are usually produced using exotic butters or tropical fats from illipe, palm mid‐fraction, sal, shea, kokum, and mango kernel. These exotic butters are often harvested from the wild trees, their supply is limited, and their quality can be varied. In this study, we report on the physicochemical and functional properties of two new CBE made from algal butter, and compared them to those of a commercial shea stearin (cSS). The functionality of these fats as CBE in a model chocolate system was assessed and compared to a cocoa butter (CB) control. The fatty‐acid composition and the triacylglycerol profile of algal butters were similar to cSS. The crystallization temperature, melting point, and crystal polymorphic form (β2 3‐L) of the algal butters were similar to those of cSS. No significant differences (P <0.05) in hardness and bloom formation were observed. One‐year storage at room temperature caused bloom formation in all chocolates, as evidenced from a βV to βVI polymorphic transformation, except for 100% algal butters and cSS. According to the results of this study, algal butter is compatible with CB and can be used as a novel CBE in chocolate products.
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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) | 0.000 | 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".