Fatty Acid Composition of Oil from Groundnuts and Oyster Nuts Grown in Uganda
Bibliographic record
Abstract
This study determined the fatty acid (FA) composition of oil from groundnuts and oyster nuts in Uganda. The FA composition was determined by Gas Chromatography/Mass Spectrometry with FID. Oil from groundnuts contained 39.71 to 55.89% oleic, 20.21 to 35.59% linoleic and 11.91 to 17.16% palmitic acids. Oil from Serenut cultivars contained cis 11-eicosenoic acid (C20.1), cis 11, 14 eicosadienoic acid (C20.2) and cis 11, 14, 17 eicosatrienoic acid (C20.3ω3) which were not detected in traditional cultivars. Oyster nut oil was high in linoleic acid at 41.02 to 44.86% and palmitic acid at 33.58 to 38.11% while oleic acid was low with amounts <10%. The polyunsaturated to saturated FA ratios of oil from groundnuts and oyster nuts were >0.45, the minimum recommended by FAO. The atherogenic (AI) and thrombogenic indices (TI) of <1 and the hypocholesterolemic to hypercholesterolemic index (h/H) of >4 in groundnut oil were favorable for cardiovascular health. Indices for oyster nut oil were ≤1for AI and >1for TI. The h/H was low compared to that of groundnut oil. Results showed little distinction between the FA compositions of oil from traditional and improved groundnuts suggesting that breeding may not have significant effect on major FAs. Oyster nut oil contained saturated and unsaturated FA in a ratio of 1:1. The lipid health indices for groundnut oil were within recommendations while those of oyster nuts were less desirable. Oyster nut oil should therefore be consumed with moderation.
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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".