UV treatment for degradation of chemical contaminants in food: A review
Bibliographic record
Abstract
Abstract Application of ultraviolet (UV) irradiation for the degradation of chemical contaminants in food products has gained more and more interest in the past two decades. The majority of the research in this field was on mycotoxins, especially aflatoxins and patulin, with limited studies on pesticide residues and other chemical contaminants in food. These studies have been focused on identifying the structure and toxicity of degradation products, investigating the influence of UV treatment factors on the degradation efficiency, determining the impact of UV treatment on the quality of food products, and developing updated UV treatment methods such as TiO 2 induced photocatalytic degradation. The summary of published literatures provided insights into future research opportunities in this area, which include determining a standard for the UV treatment description, working with naturally contaminated samples rather than artificially spiked samples, conducting pilot plant or industrial scale studies, examining more targets and conducting multi‐targets studies, and developing more innovative methods for UV treatment.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".