Commentary on: Thermal and kinetic behaviors and microscopic characteristics of 2 diacylglycerol‐enriched palm‐based oils blends by Yayuan Xu and Cao Dong
Bibliographic record
Abstract
Palm oil (PO) and its derivatives are common ingredients for making plastic fats. However, the slow crystallization of PO represents a major challenge to food manufacturers and researchers alike. The slow crystallization behavior of PO often results in a long transformation time from the α to the β′ form 1, 2 and post-hardening problems during the storage of palm-based food products 3, 4. The work presented in this paper suggests that PO and palm stearin (PS) based diacylglycerol (DAG)-enriched oils could help mitigate these problems. According to previous studies, the effects of DAGs on PO crystallization vary depending on the concentration and nature of the DAGs present. In general, DAGs promote a faster crystallization process when present at high concentrations. However, at low concentrations, several studies have reported on an inhibitory effect of palm-based DAG, up to 10%, on PO crystallization and the polymorphic transition of β′ to β 3, 5-8. On the other hand, the addition of high concentrations of palm-based DAGs, 30% and 50%, accelerated the nucleation and crystallization rate of PO and PO-palm olein blends 7, 9. Thus, it was not surprising that the DAG-enriched oil blends reported here displayed higher crystallization rates compared to PO and PS blends, since the concentration of DAGs in DAG-enriched oils was ∼50% (w/w). The authors argue that the presence of dipalmitin and the interaction between DAGs and TAGs resulted in a faster crystallization. The molecular complementarity and the melting point of DAGs influence their effect on triacylglycerol (TAG) crystallization in PO 3 and milk fat (Wright and Marangoni 2002). When DAGs have more similar molecular structures and melting points to the major TAG components in an oil, they tend to have a greater effect on crystallization 10. A stronger interaction between DAGs and TAGs results in a greater co-crystallization, formation of irregularities, and the creation of structural vacancies in the crystal network, thus leading to a delay in crystallization 10. Additionally, the asymmetrical structure of 1,2 (2,3) isomers promoted the formation of irregular crystals which retarded nucleation, whereas 1,3 isomers had an opposite effect (Fig. 1) 3. Therefore, the fatty acid positional distribution in DAGs would have helped support the argument in the present study further. The physicochemical characteristics of PO and PS based DAG-enriched blends also showed that they have a great potential for use as plastic fats. Their solid fat content (SFC)-temperature profiles meet the required SFC profiles for bakery shortenings. DAG-enriched oil blends showed a much improved structural compatibility compared to the PO and PS blends. In addition, they all retained some SFC above 45°C which is another desirable characteristic, particularly for laminating shortening. The microstructure of DAG-enriched oils was dense, homogeneous, and composed of needle-like crystals. β′ and/or β polymorphs were identified in the blends, which are commonly found polymorphic forms in roll-in shortenings 11. All these characteristics are associated with optimal functionality. The fact that these characteristics depend on the ratio of two DAG-enriched oil blends emphasizes the importance of understanding the composition and phase behavior between different DAG molecular species. The results from this study have laid the foundation for the use of DAG-enriched oil blends as a solution to solve the slow crystallization problem of PO and PS in plastic fats. In addition, the production of DAG-enriched oil through enzymatic glycerolysis has a relatively low environmental impact, a high production efficiency and can be economical 12, 13. The authors have declared no conflict of interest.
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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 | 0.001 | 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 | 0.002 |
| 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".