A Review of Strawberry Photobiology and Fruit Flavonoids in Controlled Environments
Bibliographic record
Abstract
Rapid technology development in controlled environment (CE) plant production has been applied to a large variety of plants. In recent years, strawberries have become a popular fruit for CE production because of their high economic and nutritional values. With the widespread use of light-emitting diode (LED) technology in the produce industry, growers can manipulate strawberry growth and development by providing specific light spectra. Manipulating light intensity and spectral composition can modify strawberry secondary metabolism and highly impact fruit quality and antioxidant properties. While the impact of visible light on secondary metabolite profiles for other greenhouse crops is well documented, more insight into the impact of different light spectra, from UV radiation to the visible light spectrum, on strawberry plants is required. This will allow growers to maximize yield and rapidly adapt to consumer preferences. In this review, a compilation of studies investigating the effect of light properties on strawberry fruit flavonoids is provided, and a comparative analysis of how light spectra influences strawberry's photobiology and secondary metabolism is presented. The effects of pre-harvest and post-harvest light treatments with UV radiation and visible light are considered. Future studies and implications for LED lighting configurations in strawberry fruit production for researchers and growers are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".