A 10-year analysis of RASFF notifications for mycotoxins in nuts. Trend in key mycotoxins and impacted countries
Bibliographic record
Abstract
The demand for tree nuts has significantly grown in recent years as epidemiological studies and clinical intervention trials demonstrated an inverse relationship between tree nut consumption and chronic diseases. However, mycotoxins are one of the main hazards responsible for increased "Rapid Alert System for Food and Feed" (RASFF) notifications and border rejections on nuts and nut products exported to the E.U. countries in the past few years. Mycotoxins are secondary metabolites that present serious threats to human and animal health. The most prevalent, toxic, and carcinogenic mycotoxins observed in human food and animal feed are the aflatoxins (AFs). This work analyzed notifications from the RASFF on nuts and nut products contaminated with mycotoxins, for a 10-year period from 2011 to 2021. A total of 4752 mycotoxin notifications were published on RASFF for food products worldwide, 63% (n = 3000) were notified in "nuts, nut products and seeds". It was observed that 95% (n = 2669) notifications were due to AFs. Over half of these notifications (52%, n = 1545) were reported for groundnuts, where 29% (n = 441) of the notifications were received for groundnuts from China alone. Border rejection was reported for 91% (n = 2560) of the nuts and nut products which received the notifications from the E.U. countries. This study proffers understanding into the major reasons for RASFF notifications on nuts and nut products exported to E.U. countries. Also, the implications of this issue with some recommendations that could reduce the incidents of notifications for tree nuts have been outlined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".