Preparation, characterization and phase behavior of diacid 1,3-diacylglycerols
Bibliographic record
Abstract
Diacylglycerols (DAG) play an important role in the physical and nutritional properties of many foods. As a component of most native fats and oils they promote nucleation and impede some polymorphic transitions. They are often used to stabilize emulsions in processed foods (e.g. baked goods, margarine). More recently, it has been demonstrated that replacing dietary triacylglycerols (TAG) with 1,3-DAG promotes weight loss and improves blood lipids. Our long-term goal is the production of functional 1,3-DAG fats for commercial use. However, in order to accomplish this, the crystallization and phase behavior of 1,3-DAG requires further investigation since there has been little substantive research in this area. Thus, the main focus of this study was the preparation of pure 1,3-DAG and characterization of their phase behavior. The first step involved establishing the most efficient synthesis and purification protocols for the target compounds. To this end, 12 pure 1,3-DAG were synthesized using chemical and enzyme catalysts; 11 were diacid and one was a monoacid 1,3-DAG (diacid = two different acyl groups). Depending on their melting point (Tm), compounds were purified by either recrystallization (Tm > 50°C) or flash chromatography (Tm < 50°C). For the most part, recrystallization provided higher yields and better purities. Purification by flash chromatography was confounded by the co-elution of products with by-products that have similar retention factors (Rf). The structure of target compounds was confirmed by nuclear magnetic resonance spectroscopy and the purity was ascertained by capillary gas chromatography of trirnethylsilyl derivatives on a polarizable column. Physical chemistry of the prepared compounds was assessed by differential scanning calorimetry (DSC) and x-ray powder diffraction. Phase diagrams for binary mixtures of 1,3-DAG were constructed from data obtained by DSe. The type of phase behavior observed can be predicted from the difference in T m([Delta]Tm) between the two components used to prepare a binary phase diagram - eutectic for ~Tm < 26°C and monotectic for [Delta]Tm > 30°C. Given that a similar trend has been reported for TAG, it should be possible to prepare functional 1,3-DAG fats for applications where TAG are currently in use.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".