Crystallization behaviors of diacylglycerol and triacylglycerol prepared by enzymatic catalysis
Bibliographic record
Abstract
Lipozyme RM IM was used as catalyst in the synthesis of diacylglycerol( DAG) by the transesterification of soybean oil mixed with palm stearin( melting point 45 ℃) and monoacylglycerol( MAG,prepared by soybean oil,palm stearin and glycerol),and in the synthesis of triacylglycerol( TAG) by the transesterification of soybean oil and palm stearin. Crystallization and melting behaviors of the DAG,TAG and TAG added with different proportions( 10%,30%,50% and 70%) of DAG were determined by differential scanning calorimetry( DSC),pulsed- nuclear magnetic resonance( p NMR) and polarized microscopy. The results showed that with similar fatty acid compositions,the initial crystallization temperature( 28. 14 ℃) and melting point( 48. 26 ℃) of DAG was higher than the initial crystallization temperature( 8. 51 ℃) and melting point( 30. 58 ℃) of TAG. By adding 50% and 70% of DAG,the melting point and initial crystallization temperature of TAG increased. In comparison,adding 10% and 30% of DAG had less impact on the crystallization of TAG. The solid fat content of the mixture gradually improved with the increase of addition proportion of DAG. Acicular crystals in the mixtures added with 50% and 70% of DAG were found near melting points.
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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) | 0.001 | 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".