Light‐emitting diodes (below 700 nm): Improving the preservation of fresh foods during postharvest handling, storage, and transportation
Bibliographic record
Abstract
In order to maintain the original taste, flavors, and appearance, fresh foods usually do not go through complex processing prior to sale; this makes them prone to deterioration due to external factors. Light-emitting diodes (LEDs) have many unique advantages over traditional preservation technologies leading to their increasing application in the food industry. This paper reviews the luminescence principles of LED, the advantages of LED compared with traditional lighting equipment, and its possible preservation mechanism, and then critically summarizes the beneficial effects of LED irradiation on the ripening and aging process of various fruits and vegetables (climacteric and non-climacteric). The activity changes of many enzymes closely related to crop development and quality maintenance, and the variation of flavor components caused by LED irradiation are discussed. LED illumination with a specific spectrum also has the important effect of maintaining the original color and flavor of meat, seafood, and dairy products. For microorganisms attached to the surface of animal-derived food, both 400-460 nm LED irradiation based on photodynamic inactivation principle and UV-LED irradiation based on ultraviolet sterilization principle have high bactericidal efficacy. Although there is still a lack of useful standards for matching optimal LED irradiation dose with wavelength, perhaps in the near future, the improved LED irradiation system will be applied extensively in the food 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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".