Impact of Light Intensity and Nitrogen of Nutrient Solution on Nitrate Content in Three Lettuce Cultivars Prior to Harvest
Bibliographic record
Abstract
Nitrate smoothly accumulates in leafy vegetables and poses serious health hazards if connected excessively in the human diet. The objective of this study was to improve the cultivation method of low nitrate lettuce grown in plant factory. A substantial decrease of nitrate content (NO3-) in three lettuce cultivars were subjected by short-term pre-harvest treatment combined with lowing nitrogen supply of nutrient solution to half concentration and high photosynthetic photon flux density (PPFD) by LED lighting. The lettuce (Lactuca sativa L. cv. Frill ice, Lvzhu, Ziwei) were hydroponically grown in full strength of nutrient solution based on Yamasaki formula up to harvest time within a controlled environment under two light sources using fluorescent lamps and LEDs. The results demonstrated that a subsequent 3-days treatment of additional PPFD from 250 µmol m-2 s-1 to 350 µmol m-2 s-1 from 18 days after transplanting and half concentrations of nitrogen (NO3--N) in nutrient solution which is composition to standard resulted in the decrease of nitrate content as compared to plants grown under initial PPFD and full concentration of NO3--Ncomposition. The gradual decrease in nitrate content was accompanied by an increased concentration of nutritionally carbohydrates. Another important indicator of nutritional quality such as vitamin C content exhibited some variation, fresh weight of cultivars in cv. Frill ice and Ziwei observed higher with fluorescent lamps and for cv. Lvzhu with LED treatment section followed by lowest nitrate content of fresh leaves, respectively. Further, presented results disclosed that to avoid high accumulation of nitrate in leafy vegetables, the strategy of lowering nitrogen supply and increasing light intensity prior to harvest benefiting growers and consumers by improving quality of the product also making it consumer friendly.
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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 | 0.001 |
| 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".