Effect of combined light-emitting diodes on the accumulation of glucosinolates in Brassica microgreens
Bibliographic record
Abstract
Abstract As of recent, microgreen vegetable production in controlled environments are being investigated for their bioactive properties. Phytochemicals like glucosinolates (GLS) are highly sensitive to varying spectral qualities of light, especially in leafy greens of Brassica where the responses are highly species-dependent. The accumulation of bioactive GLS were studied under 8 different treatments of combined amber (590 nm), blue (455 nm), and red (655 nm) light-emitting diodes (rbaLED). A semi-targeted metabolomics approach was carried out to profile common intact-GLS in microgreen extracts of Brassica by means of LC-HRMS/MS. Thirteen GLS were identified, among them were 8 aliphatic, 4 indolic and 1 aromatic GLS. Mass spectrometry data showed sinigrin had the highest average concentration and was highest in B. juncea, progoitrin was highest in B. rapa and glucobrassicin in R. sativus. The individual and total GLS in the microgreens of the present study were largely different under rbaLED; B. rapa microgreens contained the highest profile of total GLS, followed by R. sativus and B. juncea. Sinigrin was increased and gluconasturtiin was decreased under rbaLED lighting in most microgreens, glucoalyssin uniquely increased in R. sativus and decreased in B. rapa and glucobrassicin uniquely decreased in both B. rapa and B. juncea. The present study showed that rbaLED contributed to the altered profiles of GLS resulting in their significant modulation. Optimizing the light spectrum for improved GLS biosynthesis could lead to production of microgreens with targeted health-promoting properties. Graphical Abstract
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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".