Triacylglycerols and aliphatic alcohols from fruits of three Tunisian <i>Pistacia lentiscus</i> populations
Bibliographic record
Abstract
BACKGROUND: The search for other sources of vegetable oils by the exploitation and the enhancement of other oil plants will be needed to meet the demands of the international market. This study aims to determine the triacylglycerol (TAG) molecular species and aliphatic alcohol compositions of unexploited fruits of three Tunisian Pistacia lentiscus (lentisc) populations from the Korbous, Tebaba and Rimel areas of Tunisia. RESULTS: Results show that the content of total TAG varies from 738.32 mg g(-1) of total lipid in the Tebaba population to 981.15 mg g(-1) of total lipid in the Korbous population. Furthermore, 14 species of TAG were detected in the three studied populations. In addition, 13 aliphatic compounds were identified and classified into two groups: (1) aliphatic alcohols with fewer than 20 carbon atoms (hexadecanol, heptadecanol, (Z)-octadec-9-en-1-ol, octadecanol and nonadécanol); and (2) the policosanol group (eicosenol, docosenol, docosanol tetracosanol, hexacosanol octacosanol and triacontanol). The Tebaba population showed a distinct composition compared to Korbous and Rimel where heptadecanol is the major compound. CONCLUSION: Quantitatively, the most abundant TAG species are those constituted by palmitic, oleic and/or linoleic acid. Furthermore, the significant difference observed at the oil composition is associated with a remarkable station effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 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".