First comprehensive phenolic profiling and antioxidant assessment of red seaweed (Mazzaella japonica) using cold plasma-treated water
Bibliographic record
Abstract
Red seaweeds are emerging sources of phenolics with potential health benefits. This study presents the first detailed phenolic and antioxidant analysis of Mazzaella japonica using cold plasma-treated water (CPTW) as a green extraction medium. Free, esterified, and insoluble-bound phenolics were extracted following CPTW exposure (10, 20, and 30 min), and individual compounds were identified using HPLC-QTOF-MS/MS. CPTW treatment significantly enhanced phenolic extraction up to 20 min, particularly in the free fraction, while esterified and insoluble-bound fractions declined, suggesting cell wall disruption and hydrolysis promoted phenolic release into the free form. A total of 27 phenolic compounds, mainly phenolic acids, flavonoids, and several phlorotannins, were identified for the first time in M. japonica . Extracts exhibited strong antioxidant activity, especially in the 20-min free fraction, in both chemical assays and Caco-2 cell models. CPTW is a promising, eco-friendly technique to recover functional phenolics from seaweeds for nutraceuticals or other value-added applications. • Cold plasma-treated water (CPTW) enhanced phenolic extraction from M. japonica . • Free phenolics increased significantly after 20 min CPTW treatment. • CPTW disrupted cell walls, releasing bound phenolics into the free fraction. • 27 phenolic compounds were identified by LC-QTOF-MS/MS for the first time. • Free phenolics showed strong antioxidant activity in vitro and in Caco-2 cells.
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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) | 0.001 | 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".