Machine Vision Combined with Near-Infrared Spectroscopy to Guarantee Food Safety
Bibliographic record
Abstract
A high-speed, single-kernel robotic instrument has been developed to analyze the composition and physical characteristics of individual kernels of grain and sort them according to specified parameters. Although the automated analysis has been applied to several different types of grains, seeds, and beans, this article focuses on gluten-free ready-to-eat breakfast cereals. Oats are a gluten-free grain but can be contaminated with wheat, rye, or barley during farming, storage, transport, or other stages in the supply chain. To test for contamination, food processors must either manually check grain samples or analyze them using wet chemistry methods. These tests are both costly and time-consuming. Attempts have been made to use machine vision in analyses in order to reduce the cost and time requirements for these analyses. These attempts have failed, however, due to the similarities between various cereal grains with regard to the parameters tested—similarities that also make it difficult for human inspecto...
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".