Application of Response Surface Methodology for the Extraction of Phytochemicals from Upcycled Kale (Brassica oleracea var. acephala)
Bibliographic record
Abstract
Kale (Brassica oleracea) is recognized as a ‘superfood’ among leafy vegetables due to its high carotenoid content and potential health benefits. This study aims to optimize ultrasound-assisted extraction (UAE) to enhance the recovery of carotenoids and other phytochemicals from upcycled kale using response surface methodology. The optimized extraction parameters for carotenoids, i.e., aqueous ethanol as solvent, temperature, and extraction time at a fixed solid-to-solvent ratio, were established using the central composite design. The optimized extraction method was compared with other reported extraction methods for total phenolic content (TPC) and total antioxidant capacity (ferric reducing antioxidant power (FRAP) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging). Ultra-high-performance liquid chromatography–electrospray ionization–mass spectrometry (UPLC-ESI-MS) analysis was also performed. Under the optimized UAE conditions of 100% ethanol at 57 °C for 30 min, total carotenoid content of 392 µg/g dry weight (DW) was observed, though the predicted carotenoid content was 550 µg/g DW. Under these conditions, TPC, FRAP, and DPPH were 10.5 mg gallic acid equivalents/g DW, 13.9 µmole Trolox equivalence/g DW, and DPPH radical scavenging activity as IC50 of 2.04 ± 0.31 mg/mL, respectively. The UPLC-ESI-MS analysis showed the highest total phytochemicals recovered through microwave-assisted extraction, followed by UAE, compared to other tested extraction methods. In conclusion, the established optimized UAE process significantly enhanced the yield and quality of recovered phytochemicals from upcycled kale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".