Marketed and Original Shea Butters of Côte d’Ivoire: Physicochemical and Biochemical Characterization and Evaluation of the Potential Utilizations
Bibliographic record
Abstract
Many standards constitute shea butter trading conditions, but the exploitation of this greasy product is submitted to other industrial exigencies. The aim of this study was to characterize and evaluate the utilization potentiality of the artisanal shea butter produced in Côte d’Ivoire, on the basis of the industrials exigencies. Hence, both beige and yellow artisanal (original and market) shea butters were collected and analyzed. The refractive indexes (1.46 ± 0.00) did not vary while specific gravity at 40 °C (0.86 ± 0.00 - 0.92 ± 0.00), unsaponifiable matter (1.80 ± 0.01 - 3.76 ± 0.02%) and pH values (5.39 - 6.69) showed significant differences from a sample to another. The viscosity was very high at 40 °C (86.78 ± 0.89 - 130.10 ± 0.26 mPas) and decreased with the temperature increasing (40 to 65 °C). The UV-Vis spectrum showed a very weak absorption from 300 to 400 nm (UV-B and UV-A domains) while the near infra-red (NIR) one, revealed peaks at 450 and 700 nm for yellow shea butters only and peaks at 1200, 1400, 1725 and 2150 nm for all the samples. The fatty acids profile highlighted four main fatty acids (palmitic, stearic, oleic and linoleic acids); saturated fatty acids (56.00 ± 0.20 - 63.00 ± 0.20%) were the most important. All these interesting characteristics should arouse attention for using traditional shea butters in food, cosmetic and pharmaceutical industries.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".