Optimization of ultrasound-assisted extraction of anthocyanins from lowbush blueberries (Vaccinium Angustifolium Aiton)
Bibliographic record
Abstract
BACKGROUND: A considerable body of evidence has associated the consumption of blueberries to health-related benefits, mainly because of their anthocyanin content.The extraction of these compounds could contribute to their application in functional foods and value-added products.OBJECTIVE: In this study, we investigated the ultrasound-assisted extraction (UAE) of anthocyanins from lowbush blueberries in a bench-scale system.METHODS: Two statistical design methods, namely full factorial and Box-Behnken, were used for the screening and optimization of the variables that significantly affect the UAE of anthocyanins.Extraction temperature, time, solvent concentration (acidified ethanol), and solvent to solid ratio were selected to determine higher anthocyanin extraction (assessed by the pH-differential method).RESULTS: When evaluated by response surface methodology, solvent to solid ratio and solvent concentration had a significant effect on UAE followed by ultrasound bath temperature.The mathematical model indicated that the highest anthocyanin extraction would be obtained with 60% acidified ethanol, solvent to solid ratio of 50 mL/g, at 65 • C for 11.5 min.CONCLUSION: Ultrasound-assisted extraction was shown to be an effective method of extracting total anthocyanins from Nova Scotia lowbush blueberries.A statistical model to predict optimum conditions for extraction was developed using a Box Behnken design.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".