Unsaponifiable Compounds and Phenols Content, Antioxidant and Antitrypsin Activities of <i>Prunus persica</i> Kernel Oil
Bibliographic record
Abstract
Although peach kernels are rich in oil, there is a lack of information about its chemical and biological properties. Therefore, the purpose of this study was to determine the lipid profile, antioxidant capacity, and trypsin inhibitory propriety of peach oil extracted from two varieties (sweet cap and O'Henry) cultivated in Tunisia. The investigated peach kernel oil contains significant amount of unsaponifiable (2.1±0.5-2.8±0.2% of oil) and phenolic compounds (45.8±0.92-74.6±1.3 mg GAE/g of oil). Its n-alkane profile was characterized by the predominance of tetracosane n-C24 (47.24%) followed by tricosane n-C23 (34.43%). An important total tocopherol content (1192.83±3.1 mg/kg oil) has been found in sweet cap cultivar. Although rich in polyphenols and tocopherols, the tested oil did not display an inhibitory effect on trypsin. However, all peach oil samples showed effective antioxidant capacity and the highest values (86.34±1.3% and 603.50±2.6 μmol TE/g oil for DPPH test and ORAC assay, respectively) were observed for sweet cap oil. Peach oil has an excellent potential for application in the food and pharmaceutical industries as source of naturally-occurring bioactive substances.
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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".