Eggspectation: organic egg authentication method challenged with produce from ten different countries
Bibliographic record
Abstract
Many consumers are willing to pay a higher price for organic eggs. Since these eggs retail at a higher price than conventional eggs and their identity is difficult to verify, they are susceptible to fraud. For the authentication of Dutch eggs RIKILT developed an analytical test method based on carotenoid profiling. In the present study, the method was challenged with eggs from 10 countries. Eggs from 94 farms (65 organic, 29 conventional) were subjected to the carotenoid High Performance Liquid Chromatography - Diode Array Detection profiling combined with k-nearest neighbour classification chemometrics to predict the farming management system category: organic or conventional. The eggs from 39 of the 40 EU organic farms and the eggs of 27 of the 29 EU conventional farms, as well as eggs from 17 of the 25 organic farms from outside the EU were classified correctly. The latter lower rate was mainly due to eggs from Turkey; 78% of which were misclassified. The methodology was successful in farming management prediction of the EU eggs, as well as for eggs from Canada, Israel and Norway. The identity of the eggs from Turkey was consistently incorrectly predicted and needs further research.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".