Modeling of Bisphenol A Migration from LDPE into Food Simulants

Introduction: In recent years, there is concern about the role of Endocrine Disrupting Compounds (EDCs) in human health. As one of the EDCs, Bisphenol A (BPA) is widely used in food packaging. Migration of BPA from food packaging materials has been investigated.

Purpose: By using mathematical models, this study aims to describe the migration process of BPA from packaging system into food system.

Methods: LDPE films containing 0.1, 0.25 and 0.5 wt % BPA (Sigma-Aldrich, Milwaukee, WI) were prepared by melt mixing followed by compression molding. Reflux extraction was adopted in the determination of initial BPA concentration in LDPE films. Quantification of BPA was carried out by HPLC-UV (Waters, Milford, MA). Migration testing of BPA from LDPE into food simulants was performed according to ASTM D 4754-98, with three factors taken into account: temperature (40, 60 and 80 °C), initial BPA concentration (0.1, 0.25 and 0.5 wt % nominal) and food simulant type (water, 3% acetic acid and ethanol). Fick’s diffusion equations were applied to the migration modeling. Diffusion coefficient (D_p) and partition coefficient (K_P,F), were determined by fitting the migration curve with the diffusion equation.

Results: D_p values obtained under different conditions ranged from 10^-10 to 10^-8 cm²/s. For the interaction effect, only the interaction between temperature and food simulant type was significant (P < 0.01). The dependence of D_p on temperature followed an Arrhenius type of relationship. An exponential relationship was found between D_p and initial BPA concentration. Based on the statistical analysis, a relationship can be built to express the diffusion coefficient as a function of temperature and initial BPA concentration for each food simulant. Therefore, D_p values at other temperature and initial BPA concentration can be predicted and the migration level at different time can be calculated from the diffusion equation with those D_pvalues.

Significance: The application of modeling minimizes the experimental work while making accurate predictions.