Fruit attributes, phyto-nutrients profiles and antioxidant activity of Noni (<i>Morinda citrifolia</i> L.) genotypes in Andaman Islands
Bibliographic record
Abstract
The study aimed to indentify genotypes of Noni ( Morinda citrifolia L.) for higher recovery of phyto-constituents and with superior fruits. Tested 33 genotypes showed significant ( p <0.05) variation in total carotenoids (114.7-696.3 mg/100 g), polyphenols (165.4-370.3 mg/100 g), ascorbic acid (71.0-98.2 mg/100 g) and tannin (88.9-395.2 mg/100 g) and micronutrients Mn (4.6-284.2 ppm), Mg (16.3-1131.3 ppm), Ca (51.1-6815.6 ppm) and Cu (2.9-17.2 ppm). Fruit traits also significantly varied in the tested genotypes as observed for average fruit weight (50.5-117.9 g), number of seeds per plant (90.0-221.0) and pulp recovery (35.0-50.0%). Correlation analysis showed strong correlation between carotenoids and ascorbic acid (r=0.973; p <0.05) and flavonoids (r=0.691; p <0.05). Antioxidant activity also showed positive correlation with carotenoids (r=0.0335; p <0.05), flavonoids (r=0.249; p <0.05), Cu (r=0.953; p <0.05) and Mn (r=0.953; p <0.05). The study identified FRG-14, JGH-5, TRA-1, TRA-2 and HD-6 genotypes as rich in phytochemicals and micronutrients for commercial utilization.
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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 | 0.000 |
| 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".