Quality and authenticity of animal and vegetable fats imported to the United Arab Emirates
Bibliographic record
Abstract
This study evaluated the quality and standards compliance of animal and vegetable fats imported into the UAE through Dubai ports from 2017 to 2021. A total of 801 fat samples, originating from 35 different countries, were analyzed for compliance with UAE regulatory standards relating to peroxide value, free fatty acids, fat and moisture content, melting point, rust, and other foreign matter. Results indicated that 38% of samples were non-compliant, with significant variation between fat types. Buffalo ghee, an animal fat, showed the highest non-compliance (62%) mainly due to peroxide value and free fatty acid, while margarine exhibited the only non-compliance among vegetable fats (56%) mainly due to moisture content. Among countries with larger sample sizes, Egypt had the greatest proportion of non-compliant samples at 66%, while the United States had the lowest level of non-compliance (6.7%). Importantly, these non-compliant shipments were intercepted before reaching the market, demonstrating the effectiveness of UAE border inspections and regulatory measures. Nevertheless, the findings underline the importance of continuous quality monitoring of imported fats to maintain consumer safety. • 38% of fat samples imported to the UAE from 2017-2021 failed to meet quality standards • Buffalo ghee and margarine showed the highest non-compliance rates among fat types • Egypt had the highest proportion of non-compliant fat samples (66%), primarily buffalo ghee • Free fatty acid and peroxide values were the most common causes of non-compliance • Findings emphasize the need for stricter regulation of imported fats in the UAE market
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".