Bioactivity Studies of Different Solvent Extracts of Defatted Residues From <i>Terminalia catappa</i> L. Seed Kernels
Bibliographic record
Abstract
Terminalia catappa Linn., also referred to as tropical almond or Indian almond, can play a significant role in improving food and nutritional security. The objective of this research was to assess the antioxidant, antihyperglycemic, and antiobesity potentials of the defatted residues from seed kernels of purple and yellow cultivars. The defatted residues obtained using a micro–screw‐press oil extractor were subjected to sequential extraction using n‐hexane, dichloromethane (DCM), and methanol (MeOH) as solvents. The crude extracts of both cultivars were subjected to the evaluation of total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activities, namely, DPPH, ABTS + , and ferric reducing antioxidant power (FRAP). They were also subjected to enzyme inhibitory activities against α ‐amylase and lipase. Among the extracts, the MeOH extract of the yellow cultivar showed the highest TPC, superior antioxidant activities (DPPH and ABTS + ), and strongest enzyme inhibitory activities. In contrast, the purple cultivar exhibited the highest FRAP activity. Gallic acid was the major phenolic constituent occurring in high concentrations in the defatted residues. These findings enlighten the potential uses of defatted residues of the T. catappa seed kernels, particularly those from the yellow cultivar as an ingredient for nutraceutical and functional food applications.
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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".