Ultrasound-Assisted Extraction of Anthocyanins from Haskap (Lonicera caerulea L.) Berries Using a Deep Eutectic Solvent (DES)
Bibliographic record
Abstract
Research background. Haskap berries are one of the richest natural sources of anthocyanins and their extracts can be used for nutraceuticals and functional food ingredients. Deep eutectic solvents (DES) comprising food-grade or generally recognized as safe (GRAS) components show promise as natural solvents, but have not been applied to haskap berries. Thus, the aim of this study is to investigate the extraction of anthocyanins from haskap berries using a DES of citric acid and D-(+)-maltose. Experimental approach. The experimental approach used ultrasound-assisted extraction with a DES of citric acid and D-(+)-maltose as the solvent to achieve a sustainable green extraction process. Response surface methodology (RSM) with a Box-Behnken experimental design was used to study the effect of varying the extraction temperature, time of extraction, V(solvent)/m(sample) ratio (mL/g) and the water volume fraction (%) in the DES on the total anthocyanin content (TAC) in the haskap berry extracts. Results and conclusions. Under the optimal extraction conditions (75 °C, 10 min, 50.4 mL/g and 90 % water), a predicted TAC extraction on dry mass basis yielded 21.2 mg/g, with experimental error of 7.2 %. The TAC yield and anthocyanin profiles were similar to those obtained with conventional organic solvents. Novelty and scientific contribution. This is the first study investigating the use of a food-grade DES comprising GRAS components for the extraction of anthocyanins from haskap berries. These results indicate that the studied DES (citric acid and D-(+)-maltose) is a suitable alternative solvent for extracting anthocyanins for food-grade applications.
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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 | 0.000 |
| 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".