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The Food Microplastic Pyramid (FOMIC-Py) as a Novel Framework for Prioritizing Dietary Exposure and Industrial Processing Impact: An Italian North-South Exposure Model
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Researchers created a new tool called FOMIC-Py that ranks foods by how much they've been industrially processed (from raw ingredients to ultra-processed snacks) and found that more processing consistently means more microplastic contamination, likely from packaging and machinery. This is an early, exploratory model — not a final health risk calculator — but it suggests that choosing less-processed foods might mean swallowing fewer plastic particles, and it gives regulators a starting point for figuring out where to test food supplies more closely.
Dietary exposure to microplastics (MPs) has emerged as a significant concern; therefore, its implications for exposure characterization are presented in this study. The lack of standardized testing methods currently limits effective risk management. Determining how industrial operations contribute to the presence of these xenobiotics in the food supply chain is essential, even if environmental absorption is a recognized factor. The Food Microplastics Pyramid (FOMIC-Py), a novel hierarchical structure designed to correlate MP prevalence with industrial processing intensity, is presented in this study. The investigation suggests that technogenic inputs may represent important contributors to contamination by synthesising current literature and applying the model to regional food patterns, especially an Italian North-South scenario study. The method uses sensitivity analysis (Spearman’s ρ = 0.94) for statistical validation and classifies food items from primary commodities (Level 1) to ultra-processed items (Level 5). Mechanical abrasion and packaging interactions are recognized as the main vectors by the FOMIC-Py, which reveals a consistent accumulation of MPs across all five levels of industrial transformation. While FOMIC-Py reliably assesses particles over 1 µm, current analytical constraints regarding nanoplastics lead to a significant exposure underestimation. Consequently, rather than being an established predictive model of human target-organ dosage, the FOMIC-Py framework serves as a new exploratory, hypothesis-generating tool. The absolute exposure metrics should be evaluated cautiously owing to the underlying variability of worldwide MP extraction data, even if our statistical predictions indicate a consistent relative ranking hierarchy across contaminated food categories. These first screening criteria provide a uniform basis to direct future targeted sampling procedures and regulatory prioritization.
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