Emerging chemical and physical disinfection technologies of fruits and vegetables: a comprehensive review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With a growing demand for safe, nutritious, and fresh-like produce, a number of disinfection technologies have been developed. This review comprehensively examines the working principles and applications of several emerging disinfection technologies. The chemical treatments, including chlorine dioxide, ozone, electrolyzed water, essential oils, high-pressure carbon dioxide, and organic acids, have been improved as alternatives to traditional disinfection methods to meet current safety standards. Non-thermal physical treatments, such as UV-light, pulsed light, ionizing radiation, high hydrostatic pressure, cold plasma, and high-intensity ultrasound, have shown significant advantages in improving microbial safety and maintaining the desirable quality of produce. However, using these disinfection technologies alone may not meet the requirement of food safety and high product quality. Several hurdle technologies have been developed, which achieved synergistic effects to maximize lethality against microorganisms and minimize deterioration of produce quality. The review also identifies further research opportunities for the cost-effective commercialization of these technologies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it