Characterization of Oils and Solid Residues Obtained from <i>Bauhinia variegata L.</i> and <i>Pachira glabra pasq.</i> Seeds Through the Solvent Extraction Method
Bibliographic record
Abstract
Vegetable oils derived from non-edible seeds are excellent sources for producing biodiesel which serves as an alternative to fossil fuels. In this study, products viz vegetable oils and solid residues obtained from solvent extraction method of Bauhinia variegata and Pachira glabra seeds were characterized according to standard norms to evaluate their energy potential. The oils obtained have a free fatty acid content of 2.31 wt% and 13.6 wt%, a kinematic viscosity of 12.45 and 3.24 mm²/s, an iodine value of 17.26 and 12.37 (g of I2/100g of oil), a saponification value of 207.57 and 183.03 (mg of KOH/g of oil), a peroxide value of 10 and 8.06 (meq O2/kg of oil), and a calorific value of 40.66 and 65.08 MJ/kg, respectively. Furthermore, the physicochemical analysis of the oils revealed that they are excellent choice for biodiesel production. In addition, the proximate analysis of the solid residues of Bauhinia variegata and Pachira glabra showed high level of protein, fiber, and total carbohydrates with respective values of 34.79 and 30.41 wt%, 10.44 and 15.16 wt%, and 47.50 and 52.92 wt%. Mineral analysis indicated a high concentration of minerals, particularly potassium, sodium, calcium, and phosphorus. The solid residues exhibit anti-nutritional properties, making it suitable for various applications such as bioconversion by black soldier fly larvae, bioelectricity, biogas production, and biofuels among others.
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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".