Extraction, and Characterization of Carotenoids from 11 Allelopathic Plant Species as Potential Halal Food Colorants and Active Pharmaceutical Ingredients
Bibliographic record
Abstract
Carotenoids are a class of natural product compound that are currently being used as colouring agents and widely used in food industry. Carotenoids are bioactive pigments obtained mainly from plants through dietary intake. They possess good features in terms of dietary supplement, food colourant, and polymer stabiliser. The presence of 4 main carotenoids, which are β-carotene, zeaxanthin, lutein, and violaxanthin, were determined in 4 classes of allelopathic plant groups namely trees, ferns, grasses and herbaceous plants. This research aims to explore the carotenoid’s content and composition in 11 allelopathic species by HPLC analysis. A. auriculiformis (tree) was found to have the highest total carotenoid concentration (146.36 µg/g DW) that was substantially higher than all other species tested whereas the lowest total carotenoid concentration was found in S. palustris (fern) (3.76 µg/g DW). Lutein and β-carotene were detected highest in A. auriculiformis (tree), with 1024 ± 25.5 µg/g DW and 37.55 ± 3.16 µg/g DW, respectively. Violaxanthin and zeaxanthin were found substantially highest in M. cajuputi (tree) (5.02 ± 0.5 µg/g DW) and S. palustris (fern) (5.88 ± 0.19µg/g DW), respectively.
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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 | 0.001 |
| 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".