Microplastics in food packaging: Evaluating contamination in take out and dine in food containers and machine learning prediction of release

Pranat  Jain; Dayanand Sharma; Utkarsh; Kunwar D  Yadav; Bharat Z  Dholakiya

doi:10.62638/ZasMat1924

Authors

Pranat Jain Department of Civil Engineering, SardarVallabhbhai National Institute of Technology, Surat, Gujarat, India Author https://orcid.org/0000-0002-7194-8080
Dayanand Sharma Department of Civil Engineering, Sharda University, Greater Noida,, U.P., India Author https://orcid.org/0000-0002-2138-2218
Utkarsh Department of Civil Engineering, Sharda University, Greater Noida,, U.P., India Author https://orcid.org/0000-0003-1681-2735
Kunwar D Yadav Department of Civil Engineering, SardarVallabhbhai National Institute of Technology, Surat, Gujarat, India Author https://orcid.org/0000-0002-8316-278X
Bharat Z Dholakiya Department of Chemistry, SardarVallabhbhai National Institute of Technology, Surat, Gujarat, India Author https://orcid.org/0000-0002-9003-1462

DOI:

https://doi.org/10.62638/ZasMat1924

Abstract

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.

Keywords:

MPs; Food containers; Quantification; Fourier-transform infrared spectroscopy (FTIR); Polymer degradation; Environmental exposure; Machine learning; XGBoost

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