State-of-the-art of surface-enhanced Raman spectroscopy for pathogenic bacterial identification

Rapid and accurate identification of pathogenic bacteria is essential for safeguarding public health. However, existing approaches, including conventional culture methods, microscopic examination, modern molecular biology techniques, and sophisticated instrumental analyses, still suffer from lengthy processing times, operational complexity, high costs, and susceptibility to interference. Such limitations impede meeting the increasing demand for rapid, sensitive, cost-effective, and user-friendly pathogenic bacterial identification across diverse application scenarios. Consequently, the development of more advanced identification methodologies remains a critical research objective. Surface-enhanced Raman spectroscopy (SERS), owing to its high sensitivity, rapid measurement capability, optical probing characteristics, and molecular fingerprint information, has become a focal point in pathogenic bacterial identification research and has been applied in clinical diagnostics, food safety, environmental monitoring, and agriculture. This review systematically highlights the latest advances in the field of SERS-based pathogenic bacterial identification, drawing on 126 articles published by the American Chemical Society, Elsevier, Wiley, and other leading publishers. It details key breakthroughs in substrate fabrication, sample enrichment strategies, and precise strain discrimination. It further highlights the development and application of artificial intelligence and machine learning in SERS over the past two years, emphasizing their potentially transformative impacts on the field. In addition, recent studies on SERS-based detection of pathogenic bacteria in complex clinical specimens, including blood, urine, and sputum, are examined, with particular attention to improvements in diagnostic sensitivity, specificity, and the feasibility of standardization. Overall, the unique advantages of SERS as a next-generation rapid and portable diagnostic platform are discussed.