Assessment of Contamination and Compliance in Imported Spices, Herbs, Seasonings, Coffee, and Tea in the UAE
Bibliographic record
Abstract
The physicochemical quality and safety profile of spices, herbs, seasoning, tea, and coffee imported into the United Arab Emirates (UAE) between 2017 and 2021 were evaluated in this study. A total of 6736 samples were analyzed for 55 routine tests of which 409 (6%) were found to be noncompliant for one or more criteria indicating potential adulteration or contamination of studied samples. Herbs had the highest percentage of noncompliance at 20/187 or 11% regardless of sample size compared to tea and spices, where the second and third highest percentage of noncompliance were found at 9% (87/965) and 7% (208/2953), respectively. Among the different regulatory criteria, persistent organic pollutants (POPs) were the most frequent cause of noncompliance in spices, followed by moisture content and water extract in tea samples (83 noncompliant results). Regarding the origin of the imported samples, those from India were most often rejected (155/2860) but there was a low noncompliance rate of 5.4%. In contrast, samples imported from Taiwan had the greatest non-compliance rate at 60% or 6/10 samples. This study revealed that seasonings, spices, herbs, coffee, and tea are at risk of contamination and fraud. These everyday products can pose significant danger that is often overlooked. Despite the overall low percentage of noncompliance, there is an urgent need for better quality control to protect consumers and ensure these items are safe for use.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".