Extraction of natural antioxidants from Moroccan saffron (Crocus sativus L.) using ultrasound-assisted extraction: An optimization approach with box-behnken design
Bibliographic record
Abstract
There is increasing evidence of protective health effects of natural antioxidants, such as those contained in saffron ( Crocus sativus L.), but it is challenging to extract polyphenols, which are the purported antioxidants with conventional techniques. This study developed an ultrasound-assisted extraction technique to improve the efficiency of phenolic compound extraction and assess the antioxidant activity of saffron. A response surface methodology, utilizing a Box-Behnken design, was employed to optimize three key extraction parameters: solid-to-liquid ratio, temperature, and extraction time for both saffron stigmas and corms. The total phenolic content and antioxidant activity were analyzed using the Folin-Ciocalteu and the 2,2-Diphenyl-1-picrylhydrazyl (DPPH) assays, respectively. The optimal extraction conditions for stigmas were a solid/liquid ratio of 40 mg/30 mL, with an extraction temperature of 50 °C for 15 min, resulting in a total phenolic content yield of 118.55 mg GAE/g DM and an IC 50 value of 0.023 mg/mL. For optimal conditions when extracting from corms, the extraction time was extended to 45 min, resulting in a total phenolic content of 21.18 mg GAE/g DM and a IC 50 value of 1.02 mg/mL. Results were consistent with predicted values provided by the quadratic model, which confirmed the efficacy of the Box-Behnken design in maximizing the extraction of polyphenol content from C. sativus . Although UAE has been refined at the laboratory level, additional research is needed to assess its feasibility and efficiency on an industrial scale. Exploring large-scale UAE systems, evaluating cost-effectiveness, and identifying potential adaptations for commercial production would be highly beneficial.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".