Physicochemical and Rheological Characterization of Roll‐in Shortenings
Bibliographic record
Abstract
Abstract The composition and physical properties of roll‐in shortenings, commonly rich in trans and saturated fatty acids, were investigated and compared to other specialty fats, to provide insights into the physico‐chemical origins of their functionality. Triacylglycerols and fatty acid composition, polymorphic and melting behavior, solid fat content and large deformation rheological properties were determined. Roll‐in shortenings contained higher amounts of trisaturated and unsaturated triacylglycerols (12–27 %; 47–62 %) than other shortenings (9–11 %; 6–44 %). However, all exhibited high levels of saturates and trans fatty acids and similar crystal characteristics: β′ or mixed β′ and β, irrespective of their end use. Roll‐in shortenings had comparable melting peaks (42–52 °C) but sharper melting endotherms with higher enthalpies (38.6–43.3 J/g) than other bakery fats (18.7–25.4 J/g). This was in accordance with their well‐defined short spacings, indicative of smaller crystallites with more‐ordered packing. Solid fat profiles of roll‐in shortenings were akin to all purpose and cake interesterified shortenings, but not to all‐purpose and icing shortenings which displayed substantial melting as temperature rises. Differences in large deformation rheology (yield stress: σ*, apparent Young modulus: Eapp, yield value: C) were marginal and inconsistent with their solid fat content. Roll‐in shortenings exhibited Eapp, σ and C in the order of 1–2 × 106, 4–7 × 104, and 7–29 × 104 Pa, respectively. Particularly, the σ*and C, previously established as major parameters to specify the functionality of roll‐in shortenings, were not significantly different (p > 0.05). Overall, roll‐in shortening differed from other samples in regard to molecular makeup but not greatly in their physical parameters, suggesting that triacylglycerol composition has important implications on their functionality.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".