Preparation of 1,3‐Diglyceride Microcapsules with Low Glycemic Index
Bibliographic record
Abstract
Abstract A large number of animal and clinical experiments show that 1,3‐diglyceride (1,3‐DAG) can significantly ameliorate obesity by improving blood lipid and glucose metabolism. After microencapsulation, it cannot only be used to prepare full‐nutrient food that meets the nutritional requirements of specific patients, but also improve the storage stability of oils. In this study, 1,3‐DAG microcapsules are prepared by using high‐amylose corn starch, resistant dextrin, and sodium caseinate as composite wall materials, and 1,3‐DAG with a purity of 80% as core material. Firstly, the water phase and oil phase are mixed evenly, and then the uniform emulsion is prepared by two steps of high‐speed shearing and high‐pressure homogenization. The prepared emulsion is desiccated by a spray dryer to prepare microcapsules. Taking the encapsulation rate as the judging criteria, five gradients of core and wall material are designed to obtain the optimal formula. The results show that when the amount of core material is 28% and the wall material is 70%, the encapsulation rate of 99.51% is the highest. The prepared 1,3‐DAG microcapsules have water content of 1.9% and solubility of 98.05%, the average particle size is 2.921 µm. The particles are uniform and stable, with fine color and texture, and a high oil content.
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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) | 0.000 | 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 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".