Deep Learning Networks for Non-Destructive Detection of Food Irradiation
Bibliographic record
Abstract
The authenticity of food and the guarantee of its validity has become of great importance nowadays.One of the most crucial methods for getting rid of food pollutants is food irradiation.Therefore, utilising deep learning algorithms, this work proposed an effective and non-destructive way for identifying foods that have been exposed to gamma radiation.In place of the conventional destructive method for irradiation detection, deep learning technology is suggested in this research as a quick and non-destructive alternative.The proposed method is based on the detection of the changes of the spectrum of the radiation samples.The method is tested over apple images samples, which are irradiated by 0.5, 1, 1.5, 2 and 2.5 KGy.The findings demonstrated that employing imaging processing technology, it was possible to accurately identify food that had been exposed to radiation.The deep learning boosts the score to 94% -100%, thus improving machine learning method results from 85%.The suggested method is of great importance due to its usability, fast measurement and no need for special skills or sample preparation.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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".