Machine learning-assisted fluorescence immunochromatographic detection of Salmonella typhimurium in complex food matrices via tannic acid-enhanced AIE nanoprobes.

Authors

Chenxin Ji, Yuechun Li, Yuanyuan Cheng, Zhaowen Cui, Jing Sun, Guo Hao, Yanwei Ji, Jianlong Wang

Published in

Analytica chimica acta. Volume 1425. Pages 346293. Dec 01, 2026. Epub Sep 18, 2026.

Abstract

Rapid on-site detection of Salmonella typhimurium in complex food matrices remains challenging due to the need for fluorescent probes that maintain intense emission in aqueous environments while resisting interference from salts and pH fluctuations. In this work, we explore tannic acid (TA) as a non-covalent interfacial modifier to construct SiO₂/TCPE/TA (STT) nanoparticles for fluorescence immunochromatographic assays (ICA). Density functional theory (DFT) and time-dependent DFT calculations reveal that TA interacts with the aggregation-induced emission luminogens (AIEgens) TCPE through cooperative π-π stacking and hydrogen bonding, which stabilizes the excited state, slightly narrows the energy gap, and suppresses non-radiative decay pathways.
Guided by the theoretical predictions, we synthesize STT nanoparticles featuring uniform spherical morphology, tunable size, and a TA-enriched surface. The TA modification boosts the absolute fluorescence quantum yield from 12.91% to 30.51% and extends the fluorescence lifetime from 1.79 ns to 2.57 ns. Taking advantage of these improved photophysical properties, the STT-based ICA strip achieves a visual detection limit of 500 CFU mL^-1 and a quantitative limit of 128 CFU mL^-1 for S. typhimurium. Practical applicability is confirmed in spiked milk, chicken, and lettuce samples, with recoveries ranging from 82.75% to 119.29%. Moreover, a machine-learning classifier trained on strip images from the three food matrices attains perfect classification accuracy across five concentration levels, enabling automated and objective result reading.
This work presents a non-covalent confinement strategy to engineer bright and robust AIE nanoprobes. By combining mechanistic insights from DFT calculations with experimental validation, the TA-mediated approach not only enhances fluorescence performance but also ensures matrix tolerance. The successful integration of STT nanoparticles into a lateral flow immunoassay, coupled with machine-learning-assisted interpretation, offers a rapid, sensitive, and user-friendly platform for foodborne pathogen surveillance, with strong potential for point-of-need applications.

PMID:
42951531
Bibliographic data and abstract were imported from PubMed on 29 Oct 2026.

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