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Record W2941955749 · doi:10.1021/acs.jafc.9b00819

Current Review of the Modulatory Effects of LED Lights on Photosynthesis of Secondary Metabolites and Future Perspectives of Microgreen Vegetables

2019· review· en· W2941955749 on OpenAlexafffund
Oday Alrifai, Xiuming Hao, Massimo F. Marcone, Rong Tsao

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2019
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsUniversity of GuelphAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsPhytochemicalCarotenoidBiotechnologyPhotosynthesisHealth benefitsBiologyHuman healthBiochemical engineeringFood scienceBotanyEngineeringMedicineTraditional medicineEnvironmental health

Abstract

fetched live from OpenAlex

Light-emitting diode (LED) lights have recently been applied in controlled environment agriculture toward growing vegetables of various assortments, including microgreens. Spectral qualities of LED light on photosynthesis in microgreens are currently being studied for their ease of spectral optimization and high photosynthetic efficiency. This review aims to summarize the most recent discoveries and advances in specific phytochemical biosyntheses modulated by LED and other conventional lighting, to identify research gaps, and to provide future perspectives in this emerging multidisciplinary field of research and development. Specific emphasis was made on the effect of light spectral qualities on the biosynthesis of phenolics, carotenoids, and glucosinolates, as these phytochemicals are known for their antioxidant, anti-inflammatory effects, and many health benefits. Future perspectives on enhancing biosynthesis of these bioactives using the rapidly progressing LED light technology are further discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.009
GPT teacher head0.218
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations165
Published2019
Admission routes2
Has abstractyes

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