Essentail and Toxic Metals in Lebanese Marketed Canned Food: Impact of Metal Cans
Bibliographic record
Abstract
Canned foods are frequently consumed in Lebanon, and limited reported information about metal content in Lebanese marketed canned food. The objective of this study is to assess metal content of different canned food sold in Lebanese market and the impact of metal cans on food quality. Results indicated that Fe has the highest percentage of metals in cans, some cans the % of Al was comparable to Fe and 50% of cans had Sn up to 12%. The analysis of variance (ANOVA) of each metal indicated statistically significant difference of metal levels in the different food categories, except for Cr. In food Fe, Zn, Cu, Al, and Sn levels were below the international permissible levels. But in some artichoke and mushroom Sn levels were close to EU permissible limit, a correlation existed between levels of Sn and Al in cans and the respective food. Lead highest levels were in corn and fava beans, Hg in fish, and both were below PTWI levels. Thirty percent of vegetables and legumes and 45% of fish samples had Cd levels above EU permissible level (0.1 ug/g). This finding necessitates continuous monitoring of Cd levels in canned food for providing citizens with safe food.
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 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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".