Aqueous enzymatic oil extraction from <b><i>Irvingia gabonensis</i></b> seed kernels
Bibliographic record
Abstract
Abstract The objective of this study was to extract the fat from Irvingia gabonensis kernels without using organic solvent but by using the enzyme aqueous oil extraction process. The aqueous dispersion of kernel flour of bush mango was treated with a protease (Alcalase®), a pectinase (Pectinex®) and a mixture of cell wall‐degrading enzymes (Viscozyme®) before centrifugation. The yield of oil extracted was calculated in comparison with the chemical extraction method using hexane as solvent. A central composite experimental design was used for the determination of optimized conditions. The results showed that aqueous extraction without enzyme allows recovering 27.4% of the kernel oil. When Alcalase, Pectinex and Viscozyme were added separately, the oil yields were 35.0, 42.2 and 68.0%, respectively. Optimized conditions for Viscozyme resulted in a model of oil yield with a high coefficient of determination (r2 = 0.94). These conditions were the following: kernel‐to‐water ratio 0.11–0.19, concentration of enzyme 1.4–2.0%, and time of incubation 14–18 h. Confirmation of the model led to 83.0% oil yield after treatment of the kernel flour at a kernel‐to‐water ratio of 0.16, using 2% Viscozyme for 18 h. Under the same conditions, followed by addition of 1% Alcalase for 2 h, the yield was 90.0%.
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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 | 0.000 | 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) | 0.000 | 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".