Deep learning enabled rapid detection of live bacteria in the presence of food debris

The contamination of food with pathogenic bacteria is a major public health concern, requiring rapid and accurate detection methods. Conventional approaches, such as culture-based or molecular assays, are time-consuming, labor-intensive, and often demand specialized expertise. Here, we developed a deep learning-based strategy for rapid detection and classification of live bacteria using simple white-light microscopic images of microcolonies, even in the presence of morphologically similar food debris. The model, based on ResNet50 with a Region Proposal Network, was trained on Escherichia coli, Listeria monocytogenes, Bacillus subtilis, and debris from chicken, spinach, and cheese. The model trained on bacteria misclassified debris as bacteria (24.2% false positives), whereas the model trained on both bacteria and food debris achieved 0% false positives with 100% precision and 94.4% recall. Validation with GFP-producing B. subtilis in food matrices further confirmed robust performance (mPrecision 94.6%, mRecall 92.5%). This cost-effective method enables reliable bacterial detection in complex foods within 3 h.