Use of Electron Beams in Food Preservation
Bibliographic record
Abstract
Electron-beam irradiation (EBI) is a novel, non-thermal, physical method of food preservation (processing) technology which is effective in achieving microbial decontamination, insect disinfestation and shelf-life improvement of various food- and agriculture-based commodities. This technology is economical and environmental friendly and holds several advantages over other sources of food irradiation and conventional preservation techniques. Based on the available scientific reports, EBI could prove to be a potential alternative to the current chemical fumigants used for preservation purposes. Reports available have clearly indicated the effectiveness of employing electron beams in preserving the overall qualities and extending the shelf life of various fruits, vegetables, cereals, legumes, poultry, meat and seafoods. EBI can be highly effective when combined with other conventional and non-conventional food-processing technologies. Being a recently explored technology, there are still wide gaps prevailing in the body of research that need to be filled to provide appropriate scientific evidence. In the present chapter we aim to highlight a few vital reports on the use of EBI in various food commodities and emphasize certain vital areas which need to be explored in the near future for commercialization purposes and to overcome quarantine barriers of international trade. It is expected that this novel technology will benefit both processors and consumers in the near future.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".