Differences in Leaf Color and Stage of Development at Harvest Influenced Phytochemical Content in Three Cultivars of Kale (Brassica oleracea L. and B. napus)
Bibliographic record
Abstract
Microgreens and red colored plants have been suggested to contain higher level of health promoting phytochemicals. Kale (Brassica oleracea L. and other species) is regarded as a ‘superfood’ due to its antioxidant properties. In our study, three kale cultivars (B. oleracea L. ‘Dwarf Blue Curled’ and ‘Scarlet’, and B. napus ‘Red Russian’) differing in leaf pigmentation were harvested at five different leaf developmental stages and the concentrations of bioactive antioxidants compounds were evaluated. Carotenoids were measured by high performance liquid chromatography, and spectrometry analyses were used for total phenolics and anthocyanin measurements. Red leaf kale (‘Scarlet’) was generally higher in total carotenoids, phenolics, and anthocyanins than green leaf kales (‘Dwarf Blue Curled’ and ‘Red Russian’). As kale matured, water content decreased and dry mass increased. On a dry weight basis, total carotenoids were more abundant in microgreens and young seedlings with 4 to 6 true leaves (baby greens) than adult stage. In contrast, on a fresh weight basis, baby greens or adult kales generally contained more carotenoids and total phenolic compounds than microgreens, more likely due to the lower dry mass and phytochemical accumulation at microgreen stages. Although some microgreens vegetables may contain more health promoting phytochemicals, based on our study, higher phytochemicals were detected in young seedlings or mature leaves of kale.
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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".