Quantifying tolerances or maximum residue limits of pesticide in food commodities via deep neural networks.
Journal:
Pest management science
Published Date:
Jul 16, 2025
Abstract
BACKGROUND: Quantifying tolerances or legal maximum residue limits (MRLs) of pesticide in/on food commodities is of significance to enforce the surveillance of food safety/quality and good agricultural practices (GAP). Current in silico models mostly focus on estimating field residue levels, for example via in situ or field maximum residue levels (in situ MRLs), retention time and dissipation half-life. In silico modelling of residue tolerances involves more complicated processes (e.g. in situ GAP MRLs estimation, daily dietary assessment and toxicological test), and receives little attention to the best of our knowledge.
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