Overview of the Structure‐Property Relationship in Fat Mimetics
Bibliographic record
Abstract
Fat mimetics can be categorized within three classes based on their preparation. Indirect oil structuring, direct oil structuring, and structured biphasic systems. Within fat mimetics of each of these categories, there is a complex interplay between the characteristics of the structural scaffolding and the functional properties of the material. Plasticity is of the utmost importance in fats and fat mimetics. Whether a material undergoes brittle fracture or plastic flow during deformation may be examined using large amplitude oscillatory rheology as well as back extrusion. This property is intimately tied to the number of recognizable length scales within the structural network of the material. The oil binding capacity of a fat mimetic has important implications on aspects of storage, processing, and quality of the fat mimetic and food material and may be investigated using several techniques. Microstructural characteristics impact the oil binding capacity of fat mimetics with microstructural unit and aggregate size, surface features, and distribution within space all playing important roles. For a fat mimetic to perform adequately, the oil binding capabilities and rheological properties, particularly plasticity, both of which are related to the network microstructure, must be considered. In this chapter, we also highlight the need to begin to use tribology to characterize the friction and wear properties that would be experienced during oral processing of foods containing these materials.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".