Characterization Of Coconut Oil (Cocos Nucifera L.) From Commonly Cultivated Kenyan Varieties Extracted by Different Methods
Bibliographic record
Abstract
The objective of this study was to characterize the effects of different extraction methods and varieties on the extraction yields and quality profile of the resultant coconut oil. Three mature coconut varieties (East Africa Tall, Tall Yellow and Dwarf) were collected and subjected to different oil extraction techniques (traditional method, modified traditional method, mechanical expression and soxhlet method). The quality characteristics of the oil were determined using established standard protocols. Soxhlet extraction exhibited the highest oil yield ranging from 45.4% to 58.4% followed by mechanical expression (39.2-50.1%) and the least was traditional extraction method (6.3 to 10.2%) yield depending on variety. The Dwarf variety gave significantly lower yields compared to the other varieties. The quality characteristics were within codex standards except for the high levels of free fatty acid in traditionally (0.42%) and mechanically (0.33%) extracted oil. Lauric acid was the dominant fatty acid at 47.5%-53.5% followed by myristic acid at 15.3-18.5% depending on variety and the method of extraction. The % saturated fatty acid in all varieties was >90%. Unlike in previous studies, arachidic acid was present in all varieties. The study has demonstrated that extraction methods and variety influence the oil yield and quality characteristics of coconut oil.
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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 | 0.001 |
| 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) | 0.000 | 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".