Unravelling patterns of food tolerance to pesticide residues via non-negative matrix factorization

Gaining knowledge about the maximum residue limits (MALs) of pesticides on fresh or processed foods is critical to the process of pre-harvest cultivation, post-harvest processing and storage, and the downstream safety surveillance of food commodities. In this study, we explore the available MALs of 643 pesticides on 128 foods via non-negative matrix factorization (NMF) and hierarchical clustering to gain insights into the patterns of how similar pesticides exhibit similar MALs profiles on foods. Meanwhile, NMF predicts the MALs for untested foods via the implicitly-learnt patterns without conducting in vivo testing that potentially violates ethic regulations. Clustering results show that foods with closer NMF weights commonly exhibit closer residue tolerance profiles, and pesticides with closer MALs profiles exhibit higher structural similarities. These patterns help food experts to assess the MALs of pesticides concerned on untested foods, and the determination of MRLs on foods has its mechanistic basis. Using the reverse process of NMF decomposition, we provide the predicted MALs for 24.31% pesticide-food pairs, and NMF achieves 0.9 R2 on more than 75.78% foods in terms of recreating the experimental MALs values. Only 8.6% foods achieve less than 0.7 R2. These predicted MALs are supposed to provide practical or theoretical reference to benefit the surveillance of pesticide applications and food safety control.