Bibliographic record
Abstract
Objectives: Microplastic contamination of the global food supply chain poses serious risks to food safety and human health. This narrative review evaluates the contamination levels in various food products, detection methods, associated health risks, and existing regulatory measures. Methods: This review, conducted per PRISMA guidelines, assessed global microplastic contamination in the food supply chain from 2007–2022. A comprehensive database search identified 32 eligible studies. Data were synthesized narratively across themes: contamination levels, detection methods, health risks, and regulations. Quality assessment followed SANRA and Newcastle-Ottawa guidelines to ensure transparency, reproducibility, and methodological rigor. Results: Microplastics have been found in seafood, dairy, meat, bottled water, and packaged foods, with concentrations varying based on processing and storage conditions. Common detection methods include Fourier transform infrared (FT-IR) spectroscopy, Raman spectroscopy, and scanning electron microscopy (SEM). Seafood exhibited the highest contamination due to marine plastic pollution, whereas bottled water samples showed 93% contamination. Packaged foods stored in plastic containers also had significant microplastic content. Reported health risks include oxidative stress, gut microbiota disruption, inflammation, and toxin bioaccumulation. The lack of standardized detection protocols has contributed to the variability in the reported contamination levels. Conclusion: Microplastic contamination of food is widespread and has significant implications for human health. While detection methods are improving, regulatory measures remain inconsistent. Urgent action is needed to establish standardized protocols, stricter policies, and further research to assess long-term health risks and mitigate contamination.
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 | 0.000 |
| 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".