Effect of Syzigium aromaticum and Allium sativum spice extract powders on the lipid quality of groundnuts (Arachis hypogaea) pudding during steam cooking
Bibliographic record
Abstract
Groundnut seeds (Arachis hypogaea) contain higher concentrations of unsaturated lipids which are prone to oxidation in formulated foods. This study determined the antioxidant activities of water extract powders from two spices ( Syzigium aromaticum and Allium sativum ) and their ability to preserve the quality of lipids in groundnuts pudding during steam cooking with 0, 0.5, 1, 2 and 4% of spice extract powders. Total phenolic (TPC) and flavonoid (FC) contents of extracts from S. aromaticum were 140.23 mg GAE/100g extract and FC of 110.34 mg CAE/g extract compared to values of Allium sativum extracts (54.28 mg GAE/100g extract and 34.80 mg CAE/g extract). The showed DPPH free radical scavenging activities of the extract from S. aromaticum depending on the concentration ranged from 82.15% to 97.66% and this was higher than the activities of A. sativum but comparable to the values of buthylhydroxytoluene used as control. The chemical analysis of oil extracted revealed that the addition of the spice extract powders limited the appearance of oxidation products characterized by a reduction of up to 9-fold of peroxide value, 5-fold for anisidine and 2-fold for thiobarbituric acid reactive species. In many cases, the addition of S. aromaticum spice extract powder to the pudding better prevented lipid oxidation likely because of its superior ability to scavenge peroxyl radicals (ROO . , HO . , DPPH . ). In a nutshell, the addition S. aromaticum and A. sativum spice extract powders on grilled groundnuts paste for groundnuts pudding preparation in household can help preserve its lipid quality.
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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".