Unsaturated Emulsifier-Mediated Modification of the Mechanical Strength and Oil Binding Capacity of a Model Edible Fat Crystallized under Shear
Bibliographic record
Abstract
The effects of processing using a scraped surface heat exchanger (SSHE) before and after adding unsaturated monoglyceride (UM) on blends of fully hydrogenated soybean oil (FHSO) and soybean oil (SO) were studied. Mixtures of 40:60 and 45:55 FHSO:SO were melted at 80 °C for 30 min and crystallized statically or in the SSHE (shear rate of 25 s(-1)) at a cooling rate of 9 °C/min. Upon shearing and UM addition, polymorphic transformations toward more (β) or less (β') stable forms were governed by the combination between system concentration, composition, and crystallization conditions, as determined by differential scanning calorimetry and powder X-ray diffraction. Nuclear magnetic resonance was used to measure the solid fat content (SFC) development which showed to increase with processing conditions due to the high nucleation rate induced. Processing conditions greatly affected the nano- and microcrystalline structures which were characterized by polarized light microscopy (PLM), cryogenic transmission electron microscopy (Cryo- TEM), and Scherrer analysis of the powder X-ray diffraction data. Crystallization under shear promoted the longitudinal growth of the nanoplatelets; nevertheless, meso structural elements showed a decrease in their dimensions under the same crystallization conditions. The relative oil loss determined gravimetrically was inversely related to the elastic modulus and yield stress of the sheared fat blends, and values were closer to the desirable usability ranges for bakery applications. Our results suggest that fully hydrogenated fats can be functionalized by crystallization in a SSHE and/or by judicious addition of an unsaturated emulsifier.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".