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Foodborne pathogens pose a major challenge for public health, food safety regulation, and industrial quality control. Effective surveillance, outbreak tracing, and early warning for foodborne microbial contamination require rapid, reliable detection methods. Conventional culture-based methods are still essential for regulatory confirmation since they recover viable isolates and support downstream verification. However, their long turnaround time, labor-intensive procedures, and limited throughput restrict their use in rapid screening and on-site testing. In recent years, immunological assays, nucleic acid amplification and recognition methods, biosensors, microfluidic systems, CRISPR-Cas platforms, mass spectrometry, sequencing technologies, and artificial intelligence-assisted analysis have expanded the detection toolbox. These methods improve speed, sensitivity, portability, and multiplexing capacity, but their performance still depends on food-matrix properties, sample pretreatment, and application conditions. This review compares representative methods in terms of analytical principle, sample pretreatment, sensitivity, specificity, assay time, viable-cell discrimination, field applicability, and standardization potential. In our opinion, culture-based methods are central for confirmation, while emerging technologies are better suited for rapid screening, integrated analysis, and point-of-need testing. Nevertheless, matrix interference, limited validation in naturally contaminated samples, insufficient viable/dead-cell discrimination, and weak cross-platform consistency remain key barriers to routine use.
Pan et al. (Wed,) studied this question.