Stochastic modelling of migration from polyolefins

A method is presented to predict diffusion coefficients in polyolefins using stochastic modelling. A large number of experimental diffusion coefficients, published in the literature as one dataset, was used to derive probability distributions of diffusion coefficients in the polymers low-density polyethylene and linear low-density polyethylene, medium- and high-density polyethylene, and polypropylene. An equation is proposed to describe the diffusion coefficient as a function of the molar mass of the migrant. Model parameters and standard deviations are predicted by minimizing the sum of squared errors and the residuals are used to check the assumed types of probability distribution. The experimental data can be described by a log-normal distribution. It is shown how the derived probability distributions can be used as input for migration predictions. The method presented provides information about the most likely migration results for a given packaging¿food simulant combination. This is important for prediction of the probability that a given migration limit may be exceeded