Targeted use of LEDs in improvement of production efficiency through phytochemical enrichment
Bibliographic record
Abstract
Based on available literature, ecology and economy of light emitting diode (LED) lights in plant foods production were assessed and compared to high pressure sodium (HPS) and compact fluorescent light (CFL) lamps. The assessment summarises that LEDs are superior compared to other lamp types. LEDs are ideal in luminous efficiency, life span and electricity usage. Mercury, carbon dioxide and heat emissions are also lowest in comparison to HPS and CFL lamps. This indicates that LEDs are indeed economic and eco-friendly lighting devices. The present review indicates also that LEDs have many practical benefits compared to other lamp types. In addition, they are applicable in many purposes in plant foods production. The main focus of the review is the targeted use of LEDs in order to enrich phytochemicals in plants. This is an expedient to massive improvement in production efficiency, since it diminishes the number of plants per phytochemical unit. Consequently, any other production costs (e.g. growing space, water, nutrient and transport) may be reduced markedly. Finally, 24 research articles published between 2013 and 2017 were reviewed for targeted use of LEDs in the specific, i.e. blue range (400-500 nm) of spectrum. The articles indicate that blue light is efficient in enhancing the accumulation of health beneficial phytochemicals in various species. The finding is important for global food production. © 2017 Society of Chemical Industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".