Physicochemical Potential of Balanites aegyptiaca Seed Kernel Oil from Northern Cameroon for Biodiesel valorization
Bibliographic record
Abstract
This research work reports the physicochemical potentialities of Balanites aegyptiaca kernel oil for biodiesel valorization. Balanites seed was sampling from Pitoa, Maroua and Moutourwa localities located from North Cameroon and the kernel oil was extracted using Soxhlet apparatus. The physicochemical and the free fatty acid composition of kernel oils was determined. Among the three samples, those of Pitoa shows the higher oil yield (56.6 %). All the Refractive index of these oils were ranged in ASTM and their kinematic viscosities were small compared to the EN ISO 3104 standard. The Acid values measured were all less than EN 14104 standard and the iodine values were in accordance with EN 14111 standard, whereas, the saponification value for the samples of Maroua (139.5 ± 0.07) was higher than those of Pitoa (123.6 ± 0.04) and Moutourwa (131.5 ± 0.06) and the Peroxide values measured were lower than 10 meq/Kg. Unsaturated fatty acids were higher for the sample of Maroua (78.8 %) than those of Moutourwa (76.9%) and Pitoa (77.7 %). This research work indicate that B.aegyptiaca kernel oil obtainable from North Cameroon as higher yield (50.7-56.6%). The kernel oil shows good physicochemical properties and fatty acids profile which can be valorised as a feedstock for the production of biodiesel. Place and Duration of Study: School of Chemical Engineering and Minerals Industries - Department of Chemical Engineering, University of Ngaoundere – Cameroon work takes place between October 2020 and Jun 2021.
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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".