Comparative study of conventional and pressurized liquid extraction for recovering bioactive compounds from Lippia citriodora leaves
Bibliographic record
Abstract
The extraction of bioactive compounds from Lippia citriodora leaves (Lc) has been evaluated by comparison between Pressurized Liquid Extraction (PLE) and conventional extrations combined with HPLC-ESI-TOF-MS in order to maximize recovery of phytochemicals and to know the efficiency of both methods. To achieve these goals, conventional extractions were carried out using different concentrations of ethanol and water. On the other hand, pressurized liquid extractions were performed by a Response Surface Methodology (RSM) based on a Central Composite Design 23 model to address the bioactive compounds extraction. The independent variables selected were temperature, percentage of solvent (ethanol and water) and extraction time. The response variables were extraction yield and recovery of bioactive compounds. Thus, the optimum values to maximize yield was 200 °C, 46% ethanol and 17 min. In addition, the design versatility allowed found the optimal conditions for each chemical group and to validate them. This experimental model followed by HPLC-ESI-TOF/MS analysis offer for the first time an easy, rapid, and objective manner to optimize extraction of bioactive compounds from Lc leaves by PLE, which could be used as methodology for development functional ingredients.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".