Green Solvents in the Extraction of Bioactive Compounds from Dried Apple Cultivars
Bibliographic record
Abstract
New extraction protocols, gas-expanded liquid extraction (GXLE), and ultrasound extraction (UE) have been optimized with an emphasis on using green solvents and maximizing the extraction of 14 selected phenolic compounds, including flavonoid-based compounds and phenolic acids from dried apples. The design of the experiments’ approach was applied to optimize the main extraction parameters. Fine tuning included optimization of the flow rate in GXLE and the extraction time for GXLE and UE. Optimized GXLE was carried out with CO2–ethanol–water (34/53.8/12.2; v/v/v) at a flow rate of 3 mL/min at a temperature of 75 °C and pressure of 120 bar for 30 min. UE with ethanol–water 26/74 (v/v) lasted for 10 min at 70 °C. Both methods differed in solvent consumption and sample throughput, while providing a comparable total phenolic content of 2442 µg/g with an RSD < 10% and 2226 µg/g with RSD < 6%, for GXLE and UE, respectively. Both methods were used in determining the phenolic compounds in five apple cultivars, ‘Angold’, ‘Artiga’, ‘Golden Delicious’, ‘Meteor’, and ‘Topaz’. Phenolic profiles were plotted with chlorogenic acid, catechin, epicatechin, hirsutrin, phloridzin, and guaiaverin as the main components. Statistical evaluation, including pair t-test, Bland–Altman test, and linear regression did not reveal any differences between UE and GXLE results.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".