Influence of Extraction Conditions on Ultrasound-Assisted Recovery of Bioactive Phenolics from Blueberry Pomace and Their Antioxidant Activity
Bibliographic record
Abstract
The increase in diet-related chronic diseases has prompted the search for health-promoting compounds and methods to ensure their quality. Blueberry pomace is a rich yet underutilized source of bioactive polyphenols. For these high-value bioactive molecules, ultrasound-assisted extraction (USAE) is an attractive and green alternative to conventional extraction techniques for improving purity and yields. This study aimed to assess the impact of USAE parameters (sonication time, solvent composition, solid/liquid ratio, pH and temperature) on the recovery of phenolic compounds from blueberry pomace and antioxidant activity of the extracts. Total phenolic, flavonoid and anthocyanin contents (TPC, TFC and TAC) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging activity were analysed. USAE in 50% ethanol/water was the most efficient, yielding the highest TPC (22.33 mg/g dry matter (DM)), TFC (19.41 mg/g DM), TAC (31.32 mg/g DM) and DPPH radical scavenging activity (41.79 mg Trolox/g DM). USAE in water showed the lowest values even at low (1/40) solid/liquid ratio (7.85 mg/g DM, 3.49 mg/g DM, and 18.96 mg/g DM for TPC, TFC and TAC, respectively). Decreasing the solid/liquid ratio in water or 50% ethanol significantly increased TPC, TFC, TAC and DPPH radical scavenging. With ethanol, increasing the temperature in the range 20⁻40 °C decreased TPC but increased TFC and DPPH radical scavenging activity. Anthocyanin profiles of water and ethanolic extracts were qualitatively similar, consisting of malvidin, delphinidin, petunidin and cyanidin. These findings indicate that USAE is a method of choice for extracting high-value bioactive phenolics from blueberry pomace. Selective enrichment of different phenolic fractions is possible under select extraction conditions.
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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 | 0.001 |
| 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 | 0.001 |
| 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".