Hazelnut oil migration in dark chocolate – kinetic, thermodynamic and structural considerations
Bibliographic record
Abstract
The objective of this study was to assess the effects of three storage temperatures (11, 20 and 26 °C) on the migration kinetics and equilibrium states of a model filled confection consisting of dark chocolate and a hazelnut oil-based filling. HPLC, atomic force microscopy and X-ray diffraction were used to study the migration behaviour of hazelnut oil into simulated filled confections and the associated changes in microstructure. Using a Fickean model, the mechanism for the migration of foreign triacylglycerols (TAG) into chocolate was evaluated. Deviations from Fickian diffusion were noted with increasing temperature, and resulted from the breakdown of the chocolate matrix. At higher temperatures, filled dark chocolate exhibited accelerated fat bloom formation due to the increased ingress of foreign incompatible TAG. The rate of migration and the diffusion coefficient increased 20 and 400 times, respectively, when the storage temperature was raised from 11 to 26 °C. The amplified rate of migration at elevated temperatures resulted in a confectionary product with a severe loss in quality. There was significant degradation in the texture and gloss of the product within 24 h of storage at 26 °C. However, the storage of filled dark chocolate at 11 and 20 °C showed negligible deterioration over 8 wk. Overall, the results from this study offer some insight into the optimisation for the production and storage of filled chocolates.
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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) | 0.001 | 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".