Culinary Method Affects the Antioxidant Activity of Collard Greens (Brassica oleracea)
Bibliographic record
Abstract
The antioxidant activity of collard greens was determined after exposed to eight different thermal treatments: 1) untreated raw group, 2) short simmer 3short simmer water 4) short simmer + saute’, 5) saute’ 6) long simmer 7) long simmer water 8) long simmer + saute’. After treatment, total phenolic content (TPC) expressed in gallic acid equivalents/sample concentration (GAE/conc.), 2,2-diphenyl-1-picrylhydrazyl (DPPH) and ferrous ion chelating (FIC) antioxidant assays were determined. The sauté treated group showed the highest TPC (8.2858 GAE/conc.) followed by the raw group (8.0361) and the short simmer + sauté group (7.6227). The raw group showed the highest DPPH activity (7.7952% inhibition/conc.) followed by the sauté group (7.5877) and the short simmer + sauté group (7.4753). In both of these assays the addition of a sauté treatment to either short or long simmered treatment increased the antioxidant activity of samples compared to just the short or long simmer treatment alone. Additionally both TPC and DPPH assays showed greater antioxidant activity in the cooking water reserved from a long simmer treatment compared to the reserved cooking water of a short simmer treatment suggesting significant (p<0.05) leeching of antioxidants from collard greens into the water related to the duration of aquathermal treatment. Similar trends were not found in the results of the FIC chelating assay where both long and short simmer treatment groups showed the highest chelating abilities and the reserved cooking water from both treatments showed the lowest chelating abilities. This suggests that chelators contained in collard greens were not relatively water soluble and therefore not negatively affected byaquathermal treatments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".