Potential risk of microplastics in processed foods: Preliminary risk assessment concerning polymer types, abundance, and human exposure of microplastics
Bibliographic record
Abstract
The occurrence of microplastics (MPs) has been widely reported in human foodstuffs, and their potential negative effects on human health have been brought into focus. Processed foods are more susceptible to MPs as contamination can be introduced during processing and packaging. However, the risk posed by MPs in processed foods remained unclear. This work aims to critically review the available data for MPs in 11 types of possessed foods and to conduct a preliminary risk assessment of MPs in processed foods. For a comprehensive evaluation, three indicators were selected and determined, namely chemical risk, pollution load, and estimated daily intake (EDI). Our results suggest that nori has the highest chemical risk, followed by canned fish, beverages, table salt, and other food items. In the case of pollution load, nori and milk fall into the risk category of Ⅳ and Ⅲ respectively. Table salts, bottled water, and sugar exhibited lower MPs pollution load (risk category of Ⅱ), whereas the pollution loads of other foods were calculated to be category Ⅰ. Moreover, a correlation between the pollution load of sea salts and MPs pollution level in ambient seawater was found. Regarding EDI of MPs from different processed foods, MPs intakes through bottled water (14.3 ± 3.4 n kg -1 d -1 ) and milk (6.6 ± 2.4 n kg -1 d -1 ) are significantly higher than that of the other foods (< 1 n kg -1 d -1 ). The probabilistic estimation of MPs daily intake indicated that children (19.7 n kg -1 d -1 ) are at a higher health risk than adults (female: 17.6 n kg -1 d -1 , male: 12.6 n kg -1 d -1 ). Nevertheless, the exposure dose used in toxicological studies was about 10 times higher than the MPs intake via processed foods. Therefore, we argued that MPs in processed foods only carry limited risk. Overall, this study would provide the basis for risk management of MPs in processed food products.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".