Green Light Enhances the Postharvest Quality of Lettuce During Cold Storage
Bibliographic record
Abstract
The postharvest quality of lettuce (Lactuca sativa) is significantly influenced by the lighting environment during storage. This study evaluated the effects of green LEDs at 500 nm and 530 nm, white LEDs (400–700 nm), and dark storage on lettuce quality over 14 days at 5 °C. All treatments were applied at 10 µmol m−2 s−1 under a 12 h photoperiod. Quality parameters measured included moisture loss, relative water content (RWC), photosynthetic rate, chlorophyll content (SPAD), total soluble solids (TSSs), electrolyte leakage (EL), color change (∆E), texture (crispness), and overall visual quality (OVQ). Lettuce stored under green LEDs, particularly 530 nm, exhibited superior postharvest quality. Compared to dark storage, 530 nm reduced moisture loss by 7.1%, increased RWC by 9.2%, and reduced transpiration rate. The green light preserved photosynthetic activity (43% decline vs. 77% in the dark), increased TSS, reduced color change by 42%, improved crispness by 46.1%, and limited EL to 54.5%. Shelf life was extended by approximately four days. The 500 nm treatment showed notable improvements, including an 8.4% reduction in moisture loss, 8.2% higher RWC, a smaller photosynthesis decline (25%), and the lowest EL (53.1%). It improved color retention (∆E reduced by 45.3%) and crispness (46.8%). Both green wavelengths effectively maintained lettuce quality during cold storage, with 530 nm being the most effective overall. These results suggest that targeted green LED lighting is a promising, energy-efficient strategy to preserve postharvest quality and extend shelf life in leafy greens.
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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 | 0.001 | 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".