Meat-Borne Bacterial Pathogen Detection: Conventional, Molecular and Emerging AI-Based Strategies

Area of Science:

• Food Safety and Microbiology
• Computational Biology and Bioinformatics
• Public Health

Background:

• Meat's composition supports pathogen growth, leading to significant foodborne illnesses globally.
• Key meat-borne pathogens include Salmonella, Listeria monocytogenes, E. coli, Campylobacter, Clostridium, and Staphylococcus aureus.
• Current detection methods face challenges in speed and accuracy across the meat supply chain.

Purpose of the Study:

• To review advancements in meat-borne bacterial pathogen detection.
• To emphasize the emerging role of artificial intelligence (AI) in this field.
• To present a framework for enhanced meat safety through integrated technologies.

Main Methods:

• Evaluation of biosensors, CRISPR-based assays, isothermal amplification, and metagenomics.
• Analysis of AI applications in automating signal interpretation and image-based diagnostics.
• Review of genomic databases (NCBI, EnteroBase, VFDB, ComBase, GenBank) for AI model training.

Main Results:

• AI-based systems demonstrate high recovery (96.4-104%) and bacterial capture (100%) rates.
• AI aids in early contamination prediction and surveillance.
• Genomic databases are crucial for AI training in outbreak tracking and antimicrobial resistance prediction.

Conclusions:

• AI integration into wet lab protocols requires careful modeling and calibration.
• Full-scale AI application for meat-borne pathogen surveillance is progressing.
• An integrated framework of molecular methods, genomic surveillance, and AI analytics strengthens meat safety and public health interventions.