Phenolic Compounds and Antioxidant Capacity of Sea Cucumber (Cucumaria frondosa) Processing Discards as Affected by High-Pressure Processing (HPP)
Bibliographic record
Abstract
Sea cucumber processing discards, which include mainly internal organs, represent up to 50% of the sea cucumber biomass, and are a rich source of bioactive compounds, including phenolics. This work aimed to extract free, esterified, and insoluble-bound phenolics from the internal organs of the Atlantic sea cucumber (C. frondosa) using high-pressure processing (HPP) pre-treatment. The sea cucumber internal organs were subjected to HPP (6000 bar for 10 min), followed by the extraction and characterization of phenolics. Samples were evaluated for their total contents of phenolics and flavonoids, as well as several in vitro methods of antioxidant activities, namely, free radical scavenging and metal chelation activities. Moreover, anti-tyrosinase and antiglycation properties, as well as inhibitory activities against LDL cholesterol oxidation and DNA damage, were examined. The results demonstrated that HPP pre-treatment had a significant effect on the extraction of phenolics, antioxidant properties, and other bioactivities. The phenolics in sea cucumber internal organs existed mainly in the free form, followed by the insoluble-bound and esterified fractions. Additionally, UHPLC-QTOF-MS/MS analysis identified and quantified 23 phenolic compounds from HPP-treated samples, mostly phenolic acids and flavonoids. Hence, this investigation provides fundamental information that helps to design the full utilization of the Atlantic sea cucumber species and the production of a multitude of value-added products.
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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".