Research Progress of Biosensors in the Detection of Pesticide Residues and Heavy Metals in Tea Leaves
Year: 2025
Type: article
Abstract: Tea, a worldwide prevalent beverage, is continually contaminated by pesticide residues and heavy metals, presenting considerable health concerns to consumers. Nonetheless, effective monitoring is limited by conventional detection techniques-such as gas chromatography (GC) and inductively coupled plasma mass spectrometry (ICP-MS)-which, despite their high precision, necessitate intricate pretreatment, incur substantial operational expenses, and are inadequate for swift on-site analysis. Biosensors have emerged as a viable option, addressing this gap with their exceptional sensitivity, rapid response, and ease of operation.This review rigorously evaluates recent advancements in biosensing technologies for the detection of pesticide residues and heavy metals in tea, emphasizing the mechanisms, analytical performance, and practical applicability of prominent platforms such as fluorescence, surface-enhanced Raman scattering (SERS), surface plasmon resonance (SPR), colorimetric, and electrochemical biosensors. Electrochemical and fluorescent biosensors provide the highest promise for portable, on-site use owing to their enhanced sensitivity, cost-effectiveness, and flexibility to intricate tea matrices. The paper further emphasizes upcoming techniques such multi-component detection, microfluidic integration, and AI-enhanced data processing. Biosensors provide significant potential to revolutionize tea safety monitoring, with future advancements dependent on the synergistic incorporation of sophisticated nanomaterials, intelligent microdevices, and real-time analytics across the whole "tea garden-to-cup" supply chain.
Source: Biosensors
DOI: 10.3390/bios15120778
Authors: Pin Li, Miaopeng Chen, Tianle Yao, Long Wu, Shanran Wang, Yu Han, Ying Song, Jia Yin
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,654
Award: G5359280041
OpenAlex ID: W4416722107
