Functional food candidate from Indonesian green algae Caulerpa racemosa (Försskal) J. Agardh by two extraction methods: Metabolite profile, antioxidant activity, and cytotoxic properties
Bibliographic record
Abstract
There is an urgent need to explore natural sources like Caulerpa racemosa (Försskal) J. Agardh for bioactive compounds with strong antioxidant and therapeutic potential, providing sustainable alternatives for health and pharmaceutical innovation. This study aimed to determine the phytochemical profile and biological activities of extracts from edible green algae–known as sea grapes ( C. racemosa ). This present study has successfully identified secondary metabolites through untargeted metabolomic profiling by liquid chromatography-high resolution mass spectrometry (LC-HRMS) as well as a bioactive peptide . The antioxidant activity and cytotoxicity of extracts from C. racemosa and compounds were determined. A total of 103 metabolites were identified in the C. racemosa extract obtained by the maceration (ME), while 48 metabolites were detected in the soxhlet extract (SE). The sequence of the identified peptide was ELWKTF (Glu-Leu-Trp-Lys-Thr-Phe; C 41 H 58 N 8 O) and its abundance was identified in the α-chymotrypsin hydrolysate of C. racemosa . In the antioxidant activity test, SE and purified fraction 1 (PF1) had EC 50
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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".