Label-Free Surface-Enhanced Raman Spectroscopy Coupled with Machine Learning Algorithms in Pathogenic Microbial Identification - Current Trends, Challenges, and Perspectives - Review

Aug 6, 2026

Infectious diseases driven by microbial pathogens remain a major source of global health burden, requiring prompt control and effective prevention supported by rapid diagnostics. Conventional microbial detection methods often face high complexity, limited sensitivity, and insufficient selectivity. This review highlights label-free surface-enhanced Raman spectroscopy (SERS) as a non-invasive, fast, reliable, and cost-effective diagnostic approach for detecting bacteria, viruses, and fungi in clinical contexts. It further discusses how machine learning algorithms enhance SERS spectral analysis, summarizes current challenges in SERS deployment, and outlines future perspectives for pathogenic microbial identification.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Abstract

Introduction

Microbial threats and need for rapid identification

Limits of conventional detection methods

Label-free SERS foundations and latest advances

SERS for bacteria detection

SERS for virus detection

SERS for fungi detection

Machine learning in SERS spectral analysis

Challenges and future perspectives

Research outputs 2022 to 2026 7-1-2024 Label-free surface-enhanced Raman spectroscopy coupled with machine learning algorithms in pathogenic microbial identification: Current trends, challenges, and perspectives Jia Wei Tang Quan Yuan Xin Ru Wen Muhammad Usman Alfred Chin Yen Tay See next page for additional authors Follow this and additional works at: [https://ro.ecu.edu.au/ecuworks2022-2026](https://ro.ecu.edu.au/ecuworks2022-2026) Part of the Artificial Intelligence and Robotics Commons 10.1002/INMD.20230060 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](https://doi.org/10.1002/INMD.20230060)[ ](https://doi.org/10.1002/INMD.20230060)[This Journal Article is posted at Research Online.](This Journal Article is posted at Research Online.) [https://ro.ecu.edu.au/ecuworks2022-2026/4341](https://ro.ecu.edu.au/ecuworks2022-2026/4341) Authors Jia Wei Tang, Quan Yuan, Xin Ru Wen, Muhammad Usman, Alfred Chin Yen Tay, and Liang Wang This journal article is available at Research Online: [https://ro.ecu.edu.au/ecuworks2022-2026/4341](https://ro.ecu.edu.au/ecuworks2022-2026/4341) Received: 8 December 2023 Revised: 25 January 2024 Accepted: 13 February 2024 DOI: 10. 1002/INMD.20230060 REVIEW Label‐free surface‐enhanced Raman spectroscopy coupled with machine learning algorithms in pathogenic microbial identification: Current trends, challenges, and perspectives Jia‐Wei Tang1,2 | Quan Yuan3 | Xin‐Ru Wen3 | Muhammad Usman3 | Alfred Chin Yen Tay2,4,5,6 | Liang Wang1,7,8,9 1Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China 2The Marshall Centre for Infectious Diseases Research and Training, The University of Western Australia, Perth, Australia 3The School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China 4Marshall Laboratory of Biomedical Engineering, School of Medicine, Shenzhen University, Shenzhen, China 5Marshall International Digestive Diseases Hospital, Zhengzhou University, Zhengzhou, China 6Marshall Medical Research Center, Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China 7Division of Microbiology and Immunology, School of Biomedical Sciences, The University of Western Australia, Perth, Australia 8Center for Precision Health, School of Medical and Health Sciences, Edith Cowan University, Perth, Australia 9School of Agriculture and Food Sustainability, University of Queensland, Brisbane, Australia Correspondence Liang Wang and Alfred Chin Yen Tay. Email: [healthscience@foxmail.com](healthscience@foxmail.com) and [alfred.tay@uwa.edu.au](alfred.tay@uwa.edu.au) Funding information National Natural Science Foundation of China, Grant/Award Number: 82372258; Guangdong Basic and Applied Basic Research Foundation, Grant/Award Number: 2022A1515220023; Research Foundation for Advanced Talents of Guangdong Provincial People's Hospital, Grant/Award Number: KY012023293 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

FAQs

Why are rapid and reliable microbial detection methods important?

Microbial pathogens cause significant morbidity and mortality, making prompt control and effective prevention essential. Rapid and reliable identification is critical for public health and medical diagnostics.

What advantages does label-free surface-enhanced Raman spectroscopy (SERS) offer?

SERS provides a non-invasive diagnostic route with rapid results, reliability, and cost-effectiveness. It is promising for detecting pathogenic bacteria, viruses, and fungi in clinical settings.

How do machine learning algorithms contribute to SERS-based diagnosis?

Machine learning improves the analysis of SERS spectral data, supporting more effective interpretation of spectral patterns for microbial identification. The review emphasizes the role of these algorithms in SERS spectral analysis.

What challenges and future directions are discussed for SERS application?

The review probes practical challenges facing SERS deployment and discusses prospective development paths. These points guide expectations for future progress in pathogenic microbial identification.

Aug 6, 2026

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