Microwave-assisted extraction of bioactive compounds from Urtica dioica using solvent-based process optimization and characterization
Bibliographic record
Abstract
The extraction of bioactive compounds using green technology is a crucial topic for both research and industrys. This present investigation examines the effect of different solvents, i.e., water, ethanol (80%), and NADES (ChCl: lactic acid), during microwave-assisted extraction of bioactive compounds from nettle leaves. The RSM-BBD approach was employed with power (300, 450, and 600 W), time (10, 15, and 20 min), and sample-to-solvent ratio (1:10,1:15, and 1:20) in relation to the water, ethanol, and NADES extract's TPC (mg GAE/g), TFC (mg GAE/g), and DPPH (%IA). The optimum extraction conditions obtained were 300 W, 10 min, and 1:10 sample-to-solvent ratio for water, 300 W, 17 min, and 1:10 sample-to-solvent ratio for ethanol (80%), and 300 W, 10 min, and 1:13 sample-to-solvent ratio for NADES. Additionally, TPC (mg GAE/g), TFC (mg GAE/g), DPPH (%IA), ABTS (%), and FRAP (µM AA equivalent) of optimized extracts were compared. The antimicrobial potential of water, ethanol, and NADES extracts was also investigated against pathogenic bacteria, i.e., Staphylococcus aureus, Streptococcus pyogenes, and E. coli. Furthermore, the optimized extracts were analysed using U-HPLC, GC-MS, and LC-MS. The results highlight the suitability of microwave-assisted extraction of nettle leaf extract using NADES (ChCl: lactic acid) to obtain an extract with high antioxidant activity and good antimicrobial potential.
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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 | 0.000 |
| 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".