From byproduct to a functional ingredient: Camu-camu (Myrciaria dubia) seed extract as an antioxidant agent in a yogurt model
Bibliographic record
Abstract
This work aimed to characterize the phenolic composition and in vitro antioxidant and antiproliferative properties of lyophilized camu-camu ( Myrciaria dubia ) seed extract (LCE), and to assess the effects of LCE on the antioxidant and sensory traits of yogurt. The LCE contained 46.3% (wt/wt) total phenolic content; the main compounds quantified were vescalagin, castalagin, gallic acid, procyanidin A2, and (−)-epicatechin. The LCE had antioxidant activity, as measured by different chemical assays (2,2-diphenyl-1-picrylhydrazyl, Folin–Ciocalteu reducing capacity, total reducing capacity, ferric reducing antioxidant power, and Cu 2+ chelating capacity), and inhibited the cell proliferation of HepG2 cells (human hepatoma carcinoma; IC 50 = 1,116 µg/mL) and Caco-2 cells (human colorectal adenocarcinoma epithelial cells; IC 50 = 608.5 µg/mL). In addition, LCE inhibited the in vitro activity of α-amylase, α-glucosidase, and angiotensin-converting enzyme, and protected DNA from peroxyl radical–induced scission. When added to yogurts, different concentrations of LCE (0, 0.25, 0.5, 0.75, and 1.0 g/100 g) increased the chemical antioxidant and reducing capacities. The camu-camu yogurt containing LCE at 0.25 g/100 g had an acceptance index of 84%, showing that camu-camu seed extract may be a potential ingredient for addition to yogurts.
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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".