Antioxidant Activities in Sweet Potatoes Leaves Steamed with Spices
Bibliographic record
Abstract
Sweet potato leaves contain phenolic and flavonoids compounds which give them a potential antioxidant capacity. However, these antioxidant activities can be lost during cooking due to high temperature. In the aim to enhance their antioxidant activities, sweet potato leaves were steamed with antioxidant spices. The spices were used alone or mixed: nutmeg, Guinea pepper, cloves, nutmeg-Guinea pepper, nutmeg-cloves, Guinea pepper-cloves and nutmeg-Guinea pepper-cloves. The phytochemical analysis was carried-out on total phenolic compounds and flavonoids, while the antioxidant activities were determined via free radical-scavenging and inhibition power of lipid peroxidation. The results showed that cloves total phenolic content (513.33 mg Gallic Acid Equivalent (GAE)/g dry matter (DM)) increased to 1786.67 mg GAE/g DM when mixed with Guinea pepper. Flavonoids content was high in Guinea pepper-cloves (57.17 mg Quercetin Equivalent (QE)/g DM). In sweet potato steamed leaves, total phenolic compounds content is improved by incorporating cloves (625.83 mg GAE/g DM), while flavonoids content is slightly improved by Guinea pepper (125.00 mg QE/g DM). Among spices, cloves, Guinea pepper-cloves and nutmeg IC50 values (0.45, 0.83 and 1.50 μg/ml respectively) did not differ significantly to that of vitamin C (1.67 μg/ml), indicating that they had a good antiradical activity. The capacity of spices and steamed sweet potato leaves with and without spices to inhibit lipid peroxidation was higher than that of Gallic acid which is the standard reference. Spices, particularly cloves and Guinea pepper can improve antioxidant activities during sweet potato leaves steaming.
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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".