ArticleFoods (Basel, Switzerland)2026
Interpretable Machine Learning for Predicting Enrofloxacin Residues in Fish Using a Large Literature-Derived Database.
Article in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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9 authors.
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Abstract
Enrofloxacin use in aquaculture can lead to residue accumulation in fish tissues, creating challenges for food safety control and residue risk assessment. Residue prediction across published studies remains difficult because concentrations are influenced by species, tissue matrix, administration conditions, environmental factors, and between-study heterogeneity. Here, we constructed a literature-derived database to model matrix-specific enrofloxacin concentrations in fish. After duplicate removal, fish-focused filtering, source cleaning, and exclusion of records with missing critical variables, 1254 records from 39 source groups with traceable literature identifiers were retained from 2275 extracted records. Eleven predictors were used, and eight machine learning models were trained using log1p-transformed concentrations. Under a random row-level 80:20 split, the histogram gradient-boosting decision tree achieved the best row-level performance, with R
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