Triacylglycerol and Glycerophospholipid Identification and Accumulation During Ripening of <i>Pistacia lentiscus</i> L. (Lentisc) Fruit
Bibliographic record
Abstract
Abstract Triacylglycerol (TAG) and phospholipid (PL) compositions of vegetable oils are considered a marker of quality and are often used in industry to control the purity of the oils and to detect adulteration. In this study, the TAG and PL composition of developing fruit of Pistacia lentiscus were investigated for the first time. The total TAG content was found to increase rapidly during fruit ripening from 105 to 966 mg/100 g of oil respectively between the 35th and the 175th day after fructification (DAF). During this period, 16 different molecular species of TAG were identified and quantified. POO was the major TAG from the second stage of maturation. Only four classes of PL were identified in the P. lentiscus oil: the phosphatidic acid (PA), the phosphatidylethanolamine (PE), the phosphatidylglycerol (PG) and the phosphatidylinositol (PI). The mass spectra obtained showed the presence of nine molecular species of PA, five species of PE and seven molecular species for each PG and PI classes. The total phospholipid content decreased rapidly during fruit ripening, from 45.5 % at the 15th DAF to 6.88 % at the 175th DAF.
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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 | 0.001 | 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".