Oil migration in confectionery fat matrices
Bibliographic record
Abstract
Migration of oil in food system is an important quality problem and a leading factor in products deterioration. Chocolate enrobed products are particularly vulnerable to oil migration which causes softening of chocolate coatings, hardening of filling and bloom formation. While the exact mechanism of fat and oil migration remains hypothetical and controversial, previous studies have suggested that diffusion, capillary rise and other mechanisms may operate sequentially or simultaneously. In order to propose another viewpoint to this complex process, this thesis aimed at exploring, developing and adapting new analytical tools to visualize and quantify oil migration. First, oil migration from a nile red stained oily-phase into a cocoa butter (CB) matrix was visualized and quantified using a flatbed scanner and epifluorescence light microscopy. The strong correlation observed between the concentration gradient of linoleic acid in the CB phase and the pixel intensity confirmed that nile red migrated with the oil from the creamy mixture. Then, oil migration kinetics was determined by measuring gray scale intensity variation in time due to stained oil migration. In a second step, this method was further optimized in similar two-phase model systems. The position of the dye front where the intensity of the dye is 10% of maximum (I10) was found to be the most reliable parameter to quantify oil migration rate (OMR). Then, an approach based on Fluorescence Recovery After Photobleaching (FRAP) was used to quantify nile red diffusion through different triglyceride crystal networks. The calculated components from FRAP experiments were correlated with crystal network structural characteristics and illustrated that higher permeability coefficients were significantly related to higher effective diffusion coefficient. Finally, the study of the effects of CB origin, tempering procedure and structure on OMR showed stronger crystalline networks ('e.g.' tempered matrices) are more oil migration resistant than non-tempered matrices. The negative correlation of triunsaturated TAG with the resistance to oil migration illustrated CB chemical composition is an important parameter. However, the relationship between the permeability coefficients and other factors (squared averaged particle size and crystalline domain size) suggested micro- and nanostructure may also play 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 | 0.000 |
| 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".