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A regression-based model to predict chemical migration from packaging to food

Journal of Exposure Science & Environmental Epidemiology volume 30, pages 469–477 (2020)Cite this article

Abstract

Packaging materials can be a source of chemical contaminants in food. Process-based migration models (PMM) predict the chemical fraction transferred from packaging materials to food (FC) for application in prioritisation tools for human exposure. These models, however, have a relatively limited applicability domain and their predictive performance is typically low. To overcome these limitations, we developed a linear mixed-effects model (LMM) to statistically relate measured FC to properties of chemicals, food, packaging, and experimental conditions. We found a negative relationship between the molecular weight (MW) and FC, and a positive relationship with the fat content of the food depending on the octanol-water partitioning coefficient of the migrant. We also showed that large chemicals (MW > 400 g/mol) have a higher migration potential in packaging with low crystallinity compared with high crystallinity. The predictive performance of the LMM for chemicals not included in the database in contact with untested food items but known packaging material was higher (Coefficient of Efficiency (CoE) = 0.21) compared with a recently developed PMM (CoE = −5.24). We conclude that our empirical model is useful to predict chemical migration from packaging to food and prioritise chemicals in the absence of measurements.

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Code availability

All the analyses were carried out in R version 3.5.1 [41] using the following packages: dplyr and reshape2 to rearrange the data [42, 43], ggplot2 and ggeffects to generate the figures presented [44, 45], lme4 to fit the models [46], MuMIn and hydroGOF to compute the goodness of fit measures [47, 48]. The code necessary to generate the models can be accessed upon request to the corresponding author.

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Acknowledgements

We would like to thank Tanja Radusin (Nofima, Institute for food technology), Jorunn Nilsen, and Siw Bodil Fredriksen (Norner) for their help in better understanding the properties driving migration from plastic packaging. We also thank two anonymous reviewers for their comments to improve the paper’s structure and readability. This project was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641459 (RELIEF).

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Authors and Affiliations

  1. Department of Environmental Science, Institute for Water and Wetland Research, Radboud University Nijmegen, P.O. Box 9010, 6500 GL, Nijmegen, The Netherlands

    Mélanie Douziech, A. Jan Hendriks & Mark A. J. Huijbregts

  2. Estación Biológica de Doñana, Integrative Ecology, Avd. Americo Vespucio s/n, 41001, Sevilla, Spain

    Ana Benítez-López

  3. Quantis, EPFL Innovation Park-Bâtiment D, 1015, Lausanne, Switzerland

    Alexi Ernstoff

  4. Ostfold Research, Stadion 4, 1671, Kråkerøy, Norway

    Cecilia Askham

  5. Safety & Environmental Assurance Centre, Unilever, Colworth Science Park, Bedfordshire, MK441LQ, UK

    Henry King

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Douziech, M., Benítez-López, A., Ernstoff, A. et al. A regression-based model to predict chemical migration from packaging to food. J Expo Sci Environ Epidemiol 30, 469–477 (2020). https://doi.org/10.1038/s41370-019-0185-7

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