Machine learning-enhanced MALDI-TOF MS for real-time detection of antibiotic-resistant E. coli in food processing

Antibiotic-resistant Escherichia coli in food processing poses a significant risk to public health, necessitating rapid detection methods. This study developed an innovative approach combining matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with machine learning for rapid detection of antibiotic-resistant E. coli in food processing environments. Analysis of 69 E. coli isolates from food processing facilities revealed high resistance rates, ranging from 0 % for carbapenems to 100 % for antibiotics like streptomycin and sulfamethoxazole-trimethoprim. These findings highlight serious food safety concerns and emphasize the need for rapid detection methods. Among machine learning models trained on MALDI-TOF MS data, the optimized random forest model demonstrated superior performance, achieving cross-validation accuracies within 67–97 % across different antibiotics. Validation using 28 food-sourced samples confirmed its high predictive accuracy for multiple antibiotic classes, including penicillin, chloramphenicol, sulfonamide, tetracycline, and aminoglycoside. This approach provides a rapid, accurate tool for antibiotic resistance detection, offering significant advantages for food safety monitoring in high-throughput processing environments. Future improvements should focus on enhancing (fluoro)quinolones prediction accuracy to enable comprehensive antimicrobial resistance surveillance in food production. • Developed MALDI-TOF MS machine learning approach for antibiotic-resistant E. coli detection. • Found high antimicrobial resistance rates in poultry facility bacterial isolates. • Optimized random forest classifier achieved best resistance prediction performance. • Validated 87–100 % detection accuracy on food-sourced E. coli isolates. • Created rapid detection method compatible with current food safety protocols.