Green light enhances the phytochemical preservation of lettuce during postharvest cold storage
Bibliographic record
Abstract
The postharvest lighting environment is a main factor that influences quality preservation for harvested biomass. The objective of this study was to evaluate postharvest changes in bioactive compounds of lettuce with different storage light spectra. The effects of green LEDs with peaks at 500 nm and 530 nm, white LEDs (400-700 nm), and dark storage were evaluated, where light intensity (10 μmol m-2 s-1) and photoperiod (12 h per day) were constant with air temperature at 5°C over the 14 d treatment period. Lettuce stored with 500 nm and 530 nm green LEDs exhibited 1474.5% and 1451.8% (approximately 15.7 and 15.5 times) higher antioxidant activity, respectively, compared to dark storage. Significant improvements in total phenolic content, and 67.5% and 64.8% increases in total soluble solids with 530 nm and 500 nm green LEDs over dark storage were discerned. Exposure to 530 nm green LEDs led to 128.2% (approximately 2.28 times) higher anthocyanin content, a 26.2% increase in carotenoids, and a 95% rise in flavonoid content compared to dark storage. Increases of 26.4% and 16.0% in chlorophyll a content in lettuce stored under 500 nm and 530 nm green LEDs, respectively, and 65.6% and 46.6% rises in the Chlorophyll a/b ratio were observed. Compared to dark storage, green LEDs (500 nm) resulted in a 13.5% higher total chlorophyll content. Findings underscore the positive impact of green LEDs on the nutritional quality of lettuce, providing insight for postharvest practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".