Quantifying tolerances or maximum residue limits of pesticide in food commodities via deep neural networks

Between-residues and between-foods MRLs transferability are proved to support the QSAR-based modelling of residue tolerances in/on food commodities. The DNN, feature engineered via Morgan similarity spectra and food one-hot vector, proves to be effective in setting residue tolerances. When considering GAP and field conditions, the proposed modelling strategy can be extended to or incorporate in situ field MRLs estimation. © 2025 Society of Chemical Industry.