Effect of Gamma Radiation on the Reduction of Aflatoxin in Red Pepper
Bibliographic record
Abstract
Nowadays, the reduction of aflatoxin in food commodities using radiation treatment is getting attention. Mycotoxin contaminations in agricultural commodities have significant economic implications. Aflatoxin is a very serious food insecurity issue in developing countries because of climatic conditions, agricultural practices, and storage conditions, which are conducive to fungal proliferation and toxin production. In this study, High-performance liquid chromatography was used for the separation and determination of aflatoxins. The samples were randomly collected from different markets, placed into sealed plastic bags, and prepared for testing and investigation. The samples were exposed to a dose of 2, 4, and 6 kGy of radiation. The samples were irradiated using a Co-60 Gamma-cell research irradiator (GC-220) at a dose rate of 1.5 kGy/h. After irradiation, a promising result has been found that almost 100% reduction of pathogens in each case at each dose, and a 10.96%, 34.25%, and 34.65% aflatoxin reduction in 2, 4, and 6 kGy respectively. From this study, it is clearly shown that irradiation technology is one solution for the food insecurity seen across the globe, and should be recommended for use to stakeholders, policymakers, food storage facility providers, food packaging companies, food preservation facilities providers, warehouse providers, and food item exporters.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".