Biochemical Characterization by GC-MS of Palm Kernel Oils Produced in Côte d’Ivoire
Bibliographic record
Abstract
The objective of this study was to determine biochemical composition of palm kernel oils produced and consuming in Côte d’Ivoire in order to find out those more suitable for human diet. Our preliminary investigations showed that palm kernel oils consumed in Côte d’Ivoire were those extracted from varieties Dura and Tenera of oil palm (Elaeis guineensis Jacq.) Thus, the types of oils analyzed in this study were oil extracted from the variety Dura (OD) and oil extrated from the variety Tenera (OT). The GC-MS was used to determine the biochemical composition of these oils. The results obtained show that in each oil, seventeen fatty acids were identified by GC–MS. However, among these fatty acids, undecylenic acid was identified only in OD and heptanoic acid was identified only in OT. The two types of oil are rich in saturated fatty acids. However, OD had a relatively higher unsaturated fatty acids content. For the other compounds identified, OT had significantly the highest contents of polyphenols, α-tocopherol and sterols with the predominance of β-sitosterol. These results support that palm kernel oil extracted from the variety Tenera is rich in natural compounds that could be developed as nutraceuticals and phytomedicine. However, some unexpected compounds such as lactones were also identified in the two types of oils. Moreover, it is noted that these lactones were more abundant in oil extracted from the variety Dura (OD).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".