Carotenoid Pigments of Red, Green and Brown Macroalgae Species as Potential Active Pharmaceutical Ingredients
Bibliographic record
Abstract
Active Pharmaceutical Ingredient (API) is a substance used in a finished pharmaceutical product, intended to furnish pharmacological activity or contribute direct effect in the diagnosis, cure, mitigation, treatment or prevention of disease. It also provides a direct effect in restoring, correcting or modifying physiological functions in human beings. Macroalgae, also known as seaweed, are plant-like organisms that can be found in a marine habitat. Macroalgae has been given huge concern because of its high nutritional value and short-term growth, which is only 45 days per cycle. Therefore, three red macroalgae species of Eucheuma denticulatum, Gracilaria tikvahiae and Kappaphycus striatum), as well as green and brown macroalgae species of Caulerpa lentillifera and Padina pavonica were selected to determine their carotenoids content and composition by using UV-Vis spectrophotometer and HPLC analysis. The main carotenoids identified in red, green and brown macroalgae species were zeaxanthin, lutein, ?-carotene and violaxanthin. Marked differences were observed between red, green and brown macroalgae carotenoids content and composition. Zeaxanthin and ?-carotene were detected in all red, green and brown macroalgae ranged from 3.61 to 21.30 ?g/g DW and 2.44 to 10.70 ?g/g DW respectively. Violaxanthin was found only in green macroalgae (8.93 ?g/g DW) whereas lutein was found only in red macroalgae (9.57 to 38.60 ?g/g DW). In terms of total carotenoid content, green macroalgae contained the highest amount of carotenoid (100.89 ± 14.71 ?g/g DW). The significant outcome of the research will be new natural carotenoid pigment sources as potential active pharmaceutical ingredients which can be beneficial to halal health-promoting products industry.
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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 | 0.001 | 0.001 |
| 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".