Amber, red and blue LEDs modulate phenolic contents and antioxidant activities in eight Cruciferous microgreens
Bibliographic record
Abstract
LEDs are applied in controlled environments to produce high-quality microgreens of various nutritional benefit. We investigate different ratios of amber, blue and red LEDs on the synthesis of antioxidant phytochemicals in 8 species of the Brassica genus of microgreens. Microgreens were grown under 8 different LED ratios using combined amber, blue and red ranging from 4.73–58.94%, 20.52–58.94% and 74.36–0.57%, respectively. Results indicated that the effect of the combined lighting on antioxidant activity, total phenolic contents (TPC) accumulation, or its sub-groups total flavonoid contents (TFC) and total anthocyanin contents (TAC), were species-dependent. With increasing amber and blue and concurrently decreasing red lighting, overall positive correlations were observed for TPC, TFC and antioxidant activities (DPPH and FRAP), and overall negative correlations for TAC and ORAC (p < 0.05). Current findings suggest the microgreens can be clustered into 3 groups based on phytochemical contents and sensitivity to the lighting: (i) high blue and amber dose-dependence producing high total phenolics and flavonoids content and DPPH antioxidant activity in radish, red Rambo microgreens; (ii) moderate to high sensitivity to overall lighting but no clear dose-dependence to the light in mustards Barbarossa and red kingdom; and (iii) mizunas, pac choi and other microgreens with various responses to lighting.
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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".