Future trends in using diacylglycerols in the meat sector: emphasizing their synthesis, metabolism, health benefits, and interactions with myofibrillar proteins
Bibliographic record
Abstract
With heightened awareness of cardiovascular health and obesity-particularly visceral adiposity-contemporary consumers prioritize dietary lipid quality and quantity. Substituting triacylglycerol (TAG)-based fat sources with diacylglycerol (DAG)-based sources in meat products without compromising the sensory and technological properties can fulfill consumers' demand for healthier meat products. This review delineates the metabolic pathway distinctions between DAGs and TAGs, summarizes the health benefits of DAGs, and documents their synthesis methodologies. Furthermore, it contrasts animal-derived and plant-derived DAGs while examining recent advances in plant-derived DAG emulsions. Building on this foundation, emerging research on the interaction between DAGs and myofibrillar proteins (MPs) positions them as promising fat replacers in meat products, warranting further exploration for effective quality preservation. Application prospects and implementation barriers for DAGs in emulsified meat systems are also examined. Ultimately, this comprehensive analysis offers valuable insights and guidance for developing healthier meat products and advancing the application of DAGs in the food industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".