Document Type
Journal Article
Publication Title
Interdisciplinary Medicine
Volume
2
Issue
3
Publisher
Wiley
School
Centre for Precision Health / School of Medical and Health Sciences
Funders
National Natural Science Foundation of China / Guangdong Basic and Applied Basic Research Foundation / Research Foundation for Advanced Talents of Guangdong Provincial People's Hospital
Grant Number
82372258, 2022A1515220023, Y012023293
Abstract
Infectious diseases caused by microbial pathogens remain a primary contributor to global health burdens. Prompt control and effective prevention of these pathogens are critical for public health and medical diagnostics. Conventional microbial detection methods suffer from high complexity, low sensitivity, and poor selectivity. Therefore, developing rapid and reliable methods for microbial pathogen detection has become imperative. Surface-enhanced Raman Spectroscopy (SERS), as an innovative non-invasive diagnostic technique, holds significant promise in pathogenic microorganism detection due to its rapid, reliable, and cost-effective advantages. This review comprehensively outlines the fundamental theories of Raman Spectroscopy (RS) with a focus on label-free SERS strategy, reporting on the latest advancements of SERS technique in detecting bacteria, viruses, and fungi in clinical settings. Furthermore, we emphasize the application of machine learning algorithms in SERS spectral analysis. Finally, challenges faced by SERS application are probed, and the prospective development is discussed.
DOI
10.1002/INMD.20230060
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Tang, J. W., Yuan, Q., Wen, X. R., Usman, M., Tay, A. C. Y., & Wang, L. (2024). Label‐free surface‐enhanced Raman spectroscopy coupled with machine learning algorithms in pathogenic microbial identification: Current trends, challenges, and perspectives. Interdisciplinary Medicine, 2(3). https://doi.org/10.1002/INMD.20230060