Occurrence of aflatoxins in nuts and peanut butter imported to UAE
Bibliographic record
Abstract
Nuts are an important food group that contributes to maintaining health; however, they can be a source of exposure to aflatoxins. This study was conducted from 2017 to 2021 to assess the incidence of aflatoxins in nuts and nut products imported to the UAE from 57 countries. Associations between container type and processing technique and aflatoxin levels were also analyzed. A total of 5401 samples of pistachios, peanuts, peanut butter, and mixed nuts were examined using HPLC-FLD analysis in conjunction with immunoaffinity cleanup. In nuts, non-conformity was detected in samples imported from 32 different countries. Mean aflatoxin values for the non-compliant samples ranged from 81.0 to 92.7 μg/kg in pistachios, peanuts, and mixed nuts. A significant difference ( p < 0.05) was found between mean aflatoxin levels in samples of peanut butter (29.3 μg/kg) compared to the other types of nuts. Nuts packed in containers made of fabric material had the highest mean aflatoxin levels of 108.1 μg/kg, while 29.7 μg/kg was the lowest mean level and was detected in nuts packed in glass. Ground samples had the highest aflatoxin levels (158.9 μg/kg) among processed products. This report will be valuable as a reference document in developing approaches to control nut importation and for establishing procedures that prevent food safety risks due to aflatoxin exposure. A need was underlined for the regulating authority to audit companies importing nuts, ensure safe practices are in place, and establish standards to minimize contamination and prevent the need for product rejection at the border.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".