Extraction Optimization and Antioxidant Properties of African Eggplant<i> (Solanum macrocarpon)</i> Leaf Polyphenols
Bibliographic record
Abstract
Optimization of the yield, total phenolic content (TPC), and total antioxidant activities (TAA) of polyphenol concentrates extracted from Solanum macrocarpon leaves was studied using response surface methodology. The process variables investigated included extraction temperature (30, 50, and 70°C), extraction time (2, 4, and 6 h), and dried leaf powder : water ratio (1 : 10, 1 : 20, and 1 : 30 w/v). Box–Behnken design resulted in 15 experimental runs. The results showed the following optimum extraction conditions: temperature, 49.05°C; extraction time, 243 min; leaf powder : water ratio, 1 : 22 w/v. The optimized extraction conditions gave polyphenol concentrate yield, TPC, and TAA values of 24.94%, 421.09 mg GAE/g, and 23.81 mg AAE/g, respectively. Results of the in vitro antioxidant activities of the polyphenol concentrate showed 2, 2-diphenyl-2-picrylhydrazyl hydrate, metal chelating ability, and ferric reducing ability values of 76.78%, 80.22%, and 56.46 mg AAE/g, respectively. The study concludes that the experimental values compared closely with the predicted values, which indicates suitability of the model employed for polyphenol extraction optimization from dried S. macrocarpon leaves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".