Commentary on: Thermal and kinetic behaviors and microscopic characteristics of 2 diacylglycerol‐enriched palm‐based oils blends by Yayuan Xu and Cao Dong
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».