The Proportion of Blue Light from Light-emitting Diodes Alters Microgreen Phytochemical Profiles in a Species-specific Manner
Bibliographic record
Abstract
Microgreens are specialty vegetables that contain human health-promoting phytochemicals. Typically, microgreens are cultivated in controlled environments under red and blue light-emitting diodes (LEDs). However, the impact of varying the proportions of these light qualities on the composition of diverse phytochemicals in indoor-grown microgreens is unclear. To address this problem, the levels of chlorophylls, carotenoids, ascorbates, phenolics, anthocyanins, and nitrate were examined in arugula ( Eruca sativa L.), ‘Red Russian’ kale [ Brassica napus L. subsp. napus var. pabularia (DC.) Alef.], ‘Mizuna’ mustard ( Brassica juncea L.), and red cabbage ( Brassica oleracea L. var. capitata f. rubra ) microgreens following cultivation under LEDs supplying varying proportions of blue light (5% to 30%) and red light (70% to 95%). Varying the proportion of blue light did not affect the extractable levels of total chlorophyll, total carotenoids, or nitrate in all four microgreen species. Generally, the levels of reduced and total ascorbate were greatest in arugula, kale, and mustard microgreens at 20% blue light, and a minor decrease was apparent at 30% blue light. These metabolite profiles were not impacted by the blue light percentage in red cabbage. Kale and mustard accumulated more total phenolics at 30% blue light than all other blue light regimens; however, this phytochemical attribute was unaffected in arugula and red cabbage. The total anthocyanin concentration increased proportionally with the percentage of supplied blue light up to 30% in all microgreens, with the exception of mustard. Our research showed that 20% blue light supplied from LED arrays is ideal for achieving optimal levels of both reduced and total ascorbate in all microgreens except red cabbage, and that 30% blue light promotes the greatest accumulation of total anthocyanin in indoor-grown Brassicaceae microgreens, with the exception of mustard.
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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".