Advances in Molecular Vibrational Spectroscopy for Foodborne Pathogen Detection
Ruiyun Zhou, Wenzheng Ye, Zijuan Zhang, Yang Zhang, Tingting Shen, Dachen Wang, Zhiming Guo, Shipeng Gao, Xiaobo Zou
Abstract
Foodborne pathogens threaten global public health and food supply chains, demanding rapid, accurate, and nondestructive detection to prevent outbreaks and economic losses. Conventional methods often require destructive sampling or complex preprocessing, underscoring the critical need for innovative solutions. Molecular vibrational spectroscopy, leveraging intrinsic molecular vibrations for label-free analysis, has emerged as a transformative tool to address these challenges while preserving sample integrity. This review explores advances in infrared, surface-enhanced Raman scattering, and terahertz spectroscopies for foodborne pathogen detection. It covers spectral fingerprint mechanisms, signal amplification using plasmonic nanostructures and metamaterials, and matrix interference mitigation strategies. AI-enhanced spectral interpretation, multimodal integration, and field-deployable platforms are highlighted. Molecular vibrational spectroscopy enables rapid, nondestructive, and high-throughput pathogen detection with minimal sample preparation. The combination of multimodal spectroscopy and AI analytics shows strong potential for practical food safety monitoring, with future hybrid systems expected to revolutionize pathogen surveillance across supply chains.