This study addresses the challenge of multi-component detection of veterinary drug residues in food.Based on the physicochemical properties of veterinary drug residues,a scientific grouping principle is proposed,and the chromatography-mass spectrometry method in integrated analysis technology is studied,emphasizing its theoretical advantages in improving detection sensitivity and separation.In terms of data model construction,multiple data processing techniques were used to optimize the analysis model,exploring the impact of dimensionality reduction,noise suppression,and multiple regression methods on data accuracy,and constructing a stable analysis model.When studying the analysis of performance parameters and model optimization strategies,a machine learning based model optimization and prediction method is proposed to improve detection performance from the perspectives of sensitivity,selectivity,and accuracy.
veterinary drug residuesintegrated analysis technologydata modelgroup detection
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