Computer Modeling of the Diffusion and Fate of Micro-Contaminants Such as Microplastics, Nanoparticles, or Emerging Toxins in Complex Food Matrices Based on Molecular Dynamics and Intelligent Algorithms

Micro-contaminants, including microplastics (MPs), nanoparticles (NPs), and emerging toxins like per- and polyfluoroalkyl substances (PFAS), pose escalating risks in food systems through diffusion and bioaccumulation. This study develops a computational framework integrating molecular dynamics (MD) simulations with machine learning (ML) algorithms to model their behavior in complex matrices such as dairy emulsions and meat tissues. MD simulations reveal diffusion coefficients of ۱۰^{-۹}-۱۰^{-۷} cm²/s for MPs in lipid phases, while random forest and neural network models predict fate pathways with ۹۲% accuracy. Applied to ۵۰ virtual food scenarios, the model forecasts toxin persistence up to ۳۰ days under varying temperatures. This hybrid approach provides predictive tools for risk assessment, enhancing food safety regulations.

کلیدواژه ها:

Molecular dynamics ، intelligent algorithms ، micro-contaminants ، food matrices ، microplastics ، nanoparticles ، emerging toxins ، diffusion modeling ، machine learning ، risk assessment