Optimization of Ultrasound-Assisted Extraction of Glucosinolates from Upcycled Cauliflower Using Response Surface Methodology
Bibliographic record
Abstract
This study aimed to optimize the ultrasound-assisted extraction (UAE) process using food-grade ethanol to recover glucosinolates from upcycled cauliflower through response surface methodology. The optimized extraction process was compared with traditional extraction using maceration with solvents such as methanol and acetone. The optimum UAE conditions identified for extracting glucosinolates from upcycled cauliflower were: 42% ethanol as solvent at 43 °C for 30 min. The total glucosinolate content recovered was 7400 μg sinigrin equivalence (SE)/g dry weight (DW) of biomass. The ultra-pressure liquid chromatography-electrospray ionization-mass spectrometry (UPLC-ESI-MS) analysis confirmed that the optimized UAE yielded the highest levels of glucoraphanin (1.31 ± 0.12 μg/g DW of biomass) and sulforaphane (28.2 ± 3.34 μg/g DW of biomass). The extracts possess greater antioxidant activity as determined by ferric reducing antioxidant power and DPPH radical scavenging activity. The optimized UAE process significantly enhanced the extraction of valuable phytochemical molecules from the upcycled cauliflower. Further studies should focus on evaluating their therapeutic and preventive potential for applications in nutrition and health.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".