Zhang Lintong, Wang Shuhui, Yang Wangjincheng, Liu Xinze, Wei Zenghui, Abdalla Alwaseela, Zhang Jiachen, Kong Xiangzeng, Qu Fangfang
Center for Artificial Intelligence in Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China.
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, China.
Crit Rev Anal Chem. 2025 Jul 9:1-22. doi: 10.1080/10408347.2025.2527748.
The development of efficient and accurate methods for detecting contamination in agri-foods is critical for ensuring food safety. Terahertz time-domain spectroscopy (THz-TDS), distinguished by its unique spectral characteristics and nondestructive detection capabilities, emerges as a powerful tool for analyzing agri-food safety. This review systematically examines the integration of THz-TDS with frontier technologies (machine learning [ML], metamaterials [MM], microfluidics [MF], and functional nanomaterials [FN]) to enhance detection capabilities. The article delves into the advancements achieved in detecting physical, chemical, and microbial contaminants in agri-food over the past five years (2020-2024) through the integration of THz-TDS with these frontier technologies. Based on the current state of research, this article summarizes the challenges and prospects of THz-TDS with interdisciplinary integration technologies in applications. To advance THz-TDS for agri-food safety monitoring, multidisciplinary integration is required. ML is critical for deciphering complex THz spectral datasets, while MM play a pivotal role in amplifying analyte-specific spectral signatures. FN leverage their potential high-throughput specific adsorption and plasmonic resonance properties to enhance detection sensitivity and specificity. The MF systems can reduce absorption induced by water. This review aims to provide new insights into the multidisciplinary convergence to propel THz-TDS toward transformative agri-food safety applications.
开发高效准确的农业食品污染检测方法对于确保食品安全至关重要。太赫兹时域光谱(THz-TDS)以其独特的光谱特性和无损检测能力而著称,成为分析农业食品安全的有力工具。本综述系统地研究了太赫兹时域光谱与前沿技术(机器学习[ML]、超材料[MM]、微流体[MF]和功能纳米材料[FN])的整合,以提高检测能力。本文深入探讨了过去五年(2020 - 2024年)通过将太赫兹时域光谱与这些前沿技术整合,在检测农业食品中的物理、化学和微生物污染物方面所取得的进展。基于当前研究状况,本文总结了太赫兹时域光谱与跨学科整合技术在应用中的挑战和前景。为了推进太赫兹时域光谱用于农业食品安全监测,需要多学科整合。机器学习对于解读复杂的太赫兹光谱数据集至关重要,而超材料在放大分析物特异性光谱特征方面发挥着关键作用。功能纳米材料利用其潜在的高通量特异性吸附和等离子体共振特性来提高检测灵敏度和特异性。微流体系统可以减少水引起的吸收。本综述旨在为多学科融合提供新的见解,推动太赫兹时域光谱朝着变革性的农业食品安全应用发展。