Tripalmitin‐Driven Crystallization of Palm Oil: The Role of Shear and Dispersed Particles
Bibliographic record
Abstract
Abstract While palm oil (PO) is a reliable ingredient in formulations for biscuits, cream fillings, and compound chocolates, our understanding of its crystallization behavior and physico‐chemistry pales in comparison to many other fats and oils. Phase diagrams of triacylglycerol (TAG) mixtures may be used to elucidate fat crystal polymorphism and composition of such oils, yet conditions important to the food industry such as shear speed, relevant processing temperatures, and presence of secondary ingredients are regularly overlooked. Here, the effects of shear speed ( n = 0–500 RPM), dispersion concentration (0–5 wt.%), and dispersed particle surface chemistry [silica or octadecyl‐functionalized (C18) silica] on the thermal properties of commercial PO when cooled from 60 to 20 °C at 1 °C min −1 were explored, with focus placed on PO's higher‐melting fraction. Using a series of high‐purity TAG standards, X‐ray diffraction revealed PO's higher‐melting fraction as mainly composed of tripalmitin (PPP) crystals and molecular compounds (MC) of PPP either with 1,3‐dipalmitoyl‐2‐oleoyl‐ sn ‐glycerol (POP) or with POP and 1,2‐dipalmitoyl‐3‐oleoyl‐ rac ‐glycerol (PPO) in combination, all in a double chain‐length β' (i.e., β'‐2) conformation. Shear increased the formation of lower‐melting α‐2 POP and β'‐2 MC POP:PPO:PPP crystals while depleting the system of β'‐2 MC POP:PPP and β'‐2 PPP crystals. This loss was further exacerbated by the addition of dispersed particles to the point where PPP was completely incorporated as MC and β'‐2 PPP crystals were eliminated. While heterogeneous nucleation tends to favor kinetic products of fat crystallization, the interactions between shear and surface chemistry varied between crystal types.
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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".