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Microplastics in food packaging: Evaluating contamination in take out and dine in food containers and machine learning prediction of release
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Researchers tested take-out and dine-in food containers made from four plastic types, measuring microplastics released into water under different temperatures, pH levels and exposure times. More particles were released with hot water, higher pH and longer exposure. A computer model using this data predicted release amounts accurately, finding heat and plastic type were the main factors.
Microplastics (MPs) pose a global concern because of their potential risks to ecosystems and human health. The growing demand for fast-food delivery in today’s fast-paced lifestyle increases human exposure to MPs released from food packaging containers, posing risks to food safety and well-being. This study investigates the presence of MPs in food containers used for take-out and dine-in services. Containers made from different polymers, namely polyethylene terephthalate (PET), polypropylene (PP), high-density polyethylene (HDPE) and polystyrene (PS), were analysed for microplastic abundance. Quantification was performed using three treatments: normal water (NW), hot water (HW) and cold water (CW). The influence of pH (4, 5, 7, 8 and 9), temperature (15, 30, 45, 60 and 90 °C) and exposure time (0.5, 1, 1.5 and 2 h) on MP release was examined. ATR-FTIR, SEM and AFM were employed to identify the polymer constituents and the morphology of the released particles. Average MP abundance was 474.44 ± 33.08 µg/container for NW, 659.48 ± 45.04 µg/container for HW and 556.58 ± 43.08 µg/container for CW. The highest release was obtained at pH 8, 90 °C and 2 h of exposure, with an average of 293.98 ± 13.86 particles/L. Building on the experimental data, a leakage-free machine-learning framework was developed to predict MP release from routine usage conditions. Five regression algorithms were compared, and an extreme gradient boosting model tuned with the Grey Wolf Optimizer (GWO-XGBoost) gave a coefficient of determination of 0.984 for particle count and 0.989 for released mass on unseen experimental conditions, with a negligible train-to-test gap. Shapley additive explanations (SHAP) showed that thermal exposure and polymer type were the dominant controllable drivers of release. The combined experimental and data-driven approach clarifies the fate of MPs in food packaging and offers a practical tool to support mitigation and control.
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