Agave angustifolia Haw. Leaves as a Potential Source of Bioactive Compounds: Extraction Optimization and Extract Characterization
Bibliographic record
Abstract
The leaves of Agave angustifolia Haw. are the main agro-waste generated by the mezcal industry and are becoming an important source of bioactive compounds, such as phenolic compounds, that could be used in the food and pharmaceutical industries. Therefore, the extraction and identification of these phytochemicals would revalorize these leaf by-products. Herein, maceration and supercritical carbon dioxide (scCO2) extractions were optimized to maximize the phenolic and flavonoid contents and the antioxidant capacity of vegetal extracts of A. angustifolia Haw. In the maceration process, the optimal extraction condition was a water–ethanol mixture (63:37% v/v), which yielded a total phenolic and flavonoid content of 27.92 ± 0.90 mg EAG/g DL and 12.85 ± 0.53 µg QE/g DL, respectively, and an antioxidant capacity of 32.67 ± 0.91 (ABTS assay), 17.30 ± 0.36 (DPPH assay), and 13.92 ± 0.78 (FRAP assay) µM TE/g DL. Using supercritical extraction, the optimal conditions for polyphenol recovery were 60 °C, 320 bar, and 10% v/v. It was also observed that lower proportions of cosolvent decreased the polyphenol extraction more than pressure and temperature. In both optimized extracts, a total of 29 glycosylated flavonoid derivatives were identified using LC-ESI-QTof/MS. In addition, another eight novel compounds were identified in the supercritical extracts, showing the efficiency of the cosolvent for recovering new flavonoid derivatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".