Polyphenol characterization, anti-oxidant, anti-proliferation and anti-tyrosinase activity of cranberry pomace
Bibliographic record
Abstract
Background: Cranberry pomace (CP), an underutilized by-product from juice processing, contains a wide range of biologically active compounds that can be recovered and used in a variety of applications in functional foods and nutraceuticals. Methods: In this study, analytical chemical techniques such as solvent extractions and characterization of extracts in respect with their phenolic content were performed using ultra-high performance liquid chromatography mass spectrometry (UPLC-MS) and spectrophotometry. Crude CP extract and its phenolic acids, flavonols, anthocyanins and proanthocyanidins–rich fractions were then evaluated for their anti-oxidant capacity, tyrosinase inhibitory activity, and anti-proliferation activity against hepatocellular carcinoma HepG2 cells. Results: On a dry weight basis, the different CP fractions contained seven major anthocyanins (0.1-125 mg/g), six major phenolic acids (0.8-31 mg/g), seven flavonols (1-126 mg/g) and five flavan-3-ols (0.1-12 mg/g). Fractions rich in flavonols exhibited the most potent antioxidant capacities with ferric ion reducing antioxidant power values of 1.8-1.9 mmole/g and 2, 2-diphenyl-1-picrylhydrazyl radical scavenging IC50 values of 15.1-15.2 mg/L respectively. On the other hand, fractions rich in phenolic acids and flavan-3-ol monomers demonstrated the most potent anti-tyrosinase activity (IC50=6.1-6.2 mg/L) and anti-proliferative activity (IC50=7.8-15.8 mg/L). Generally, all the fractions exhibited a dose-response relationship in the selected biological activity assays.Conclusion: This study suggests an effective utilization of CP to obtain biologically active fractions with potential to be used in functional foods and nutraceuticals designed for the prevention of chronic diseases associated with oxidative stress.Keywords: cranberry pomace, polyphenol, anti-tyrosinase, anti-proliferation, nutraceuticals, functional foods
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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 | 0.001 | 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".