Physicochemical Properties and Antioxidant Activity of Mixed Oil of Safou (Dacryodes edulis (G. Don) H.J. Lam) from Several Trees
Bibliographic record
Abstract
The valorization of lipids can be highlighted by industrial exploitation of safou pulp, very rich in these. However, the low yield of safou trees is lacking. It’s necessary to estimate the technological potentialities of the oil obtained in order to describe the nutritional value and potential exploitation of this oil. Physical and chemical characteristics of this oil were examined. The aim of our study was to extract oil from fruits of several safou fruits and prepared a unique sample. This sample was essayed for its physicochemical properties and antioxidant activity. Results showed that the refractive index was 1.4693. The density and viscosity values were 0.9 mg/mL and 31.08 mPa/s, respectively. Acid and peroxide values were 6.17 mg KOH/g and 31.46 meq O2/kg. Gas chromatography revealed that the major fatty acids were C16:0 (44.23%), C18:1 (30.50%), and C18:2 (19.62%). Triacylglycerols were the most important lipids (88.88% of total lipids). Spectrometric assessment of color led to the remarkable presence of the peaks associated with the visible absorption of carotenoids near 530 nm and chlorophyll pigments located between 610 and 670 nm. Antioxidant activity and DPPH radical scavenging activities of safou oil were exanimate. So, oil mixtures can be used, while varietal delimitation and mix some varieties for oil industry.
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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) | 0.000 | 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".