Pretreatment-driven intensification of ultrasound-assisted extraction of phenolic compounds from microwave-dried pea haulm biomass
Bibliographic record
Abstract
Pea haulm was valorized by integrating pretreatments with ultrasound-assisted extraction after microwave-assisted hot-air drying (200 W; 40 °C, 60 °C, 80 °C). Drying severity bleached color (lightness, 76.9 to 93.4) and decreased extractables: total phenolic content (TPC, 17.2 to 14.5 mg GAE/g dry matter (DM)), total flavonoid content (TFC, 8.1 to 6.6 mg CTE/g DM), 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity (DPPH, 115.7 to 109.0 μmol TE/g DM), and ferric reducing antioxidant power (FRAP, 69.1 to 58.7 μmol TE/g DM). At 40 °C, pretreatments intensified extraction: high pressure maximized TPC (21.4 mg GAE/g DM) and TFC (13.7 mg CTE/g DM) with higher DPPH (132.2 μmol TE/g DM); pulsed electric field yielded the highest FRAP (77.5 μmol TE/g DM). These results showed that gentle drying (200 W_40 °C) and high-pressure pretreatment enhance ultrasound-assisted extraction of phenolic compounds from pea haulm, offering potential for sustainable production of antioxidant-rich bio-ingredients. • 200W, 40°C drying best preserves color and extractable phenolics. • HP, PEF, homogenization pretreatments intensify UAE of pea haulm bioactives. • High pressure maximizes TPC/TFC and DPPH; PEF gives highest FRAP. • Pretreatments raise surface opening (HP>PEF>homogenization). • Higher drying temp shifts proteins from α-helix to β-sheet/coil.
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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) | 0.001 | 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".