A Comparison of Unconventional Microwave and Ultrasound-Assisted Extraction Methods used for Flavonoids
Bibliographic record
Abstract
Cocoa pods (Theobroma cacao L.) are a rich source of flavonoids, which are natural antioxidants known for their health benefits. This study investigated the use of microwave-assisted extraction (MAE) and ultrasound-assisted ex-traction (UAE) to extract the maximum flavonoids and antioxidants from cocoa pods. MAE and UAE are efficient and sustainable methods for extracting bioactive compounds like flavonoids and antioxidants from cocoa pods, offer-ing faster extraction, reduced solvent use, and better compound preservation compared to conventional methods. These technologies unlock the untapped potential of cocoa pods for applications in food, cosmetics, and pharmaceuti-cals. The effects of extraction time (2–10 min), microwave power (100–300 W), and the ratio of cocoa husk powder to solvent (0.02–0.06 g/mL) were evaluated for MAE. Meanwhile, for UAE, different temperatures (30–50 °C), times (10–30 min), and powers (16–48 W) were studied. The Design-Expert® software with Response Surface Methodology (RSM) and a Box-Behnken Design was utilized to analyze the effects of a combination of these parameters. The re-sults showed that extraction time, microwave power, and the ratio of cocoa husk powder–solvent significantly affected the total flavonoid yield and antioxidant capacity. The highest total flavonoid yield obtained by the MAE method (123.07 ppm) was at an extraction time of 10 min, cocoa husk–solvent ratio of 0.06 mg/mL, and microwave power of 300 W. The extract obtained by the MAE method showed an excellent antioxidant capacity of 43.49 mg/mL GAEAC, and a robust antioxidant activity indicated by an IC50 value of 42.19. Conversely, the highest total flavonoid yield of the UAE method (8.45 mg/mL GAEAC) was achieved at 30 min, 40 °C, and 40 W. The extract from the UAE method demonstrated a better antioxidant capacity (7.51 mg/mL GAEAC) and antioxidant activity (IC50 value of 23.46) than that from the MAE technique
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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 | 0.001 |
| 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 | 0.001 | 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".