Effect of photoselective filters on the physical and chemical traits of vine-ripened tomato fruits
Bibliographic record
Abstract
The effects of several wavelength selective light filters placed on developing mature green tomato fruits were studied to determine whether light environment during fruit ripening has an impact on fruit composition. Juice titratable acidity and fruit fresh and dry weight varied little with the different filters. Reducing the red/far-red light ratio with a green filter to simulate vegetation shade slightly delayed ripening. Reducing infrared light (700-1100 nm) reduced vitamin C and soluble sugars content. A drastic reduction in photosynthetic light (97%) reduced both β-carotene (-23%) and lycopene (-29%) contents and red coloration (-21%). Significant correlations were found among the content of soluble sugars, vitamin C and lycopene, but these components increased differently according to the spectral composition of the light transmitted to the fruit. The content of lycopene and β-carotene increased with exposure to photosynthetic radiation and more precisely with exposure to blue light. In contrast, the content of vitamin C and sugar increased with infrared light exposure. Key words: β-carotene, photoselective filters or shading, fruit ripening, fruit environment, lycopene, Lycopersicon esculentum, tomato, vitamin C
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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) | 0.002 | 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".