Machine learning-assisted NaEuF₄ nanoparticle-enhanced time-resolved fluorescence immunoassay for ultrasensitive and visual determination of aflatoxin B₁.

Authors

Yufeng Yan, Dian Li, Shangbin Kao, Feifan Cheng, Canlong Yan, Yuan Qin, Xiumei Zhou, Lanying Liu, Xueqin Zhao, Biao Huang

Published in

Mikrochimica acta. Volume 193. Issue 11. Oct 27, 2026. Epub Oct 27, 2026.

Abstract

An ultra-sensitive time-resolved fluorescence immunoassay (U-TRFIA) based on NaEuF₄ nanoprobes was developed for the detection of aflatoxin B₁ (AFB₁) in grain samples, and machine learning was further integrated to assist in signal interpretation. The synthesized NaEuF₄ nanoprobes, characterized by high Eu³⁺ loading and stability, were used as signal-amplifying probes to label the secondary antibody. Based on an indirect competitive assay, this method achieved an extremely low limit of detection (LOD) of 20 pg/mL, with an IC₅₀ of 34.8 pg/mL and a detection range of 20-500 pg/mL. Compared to conventional TRFIA, this platform offers a fivefold increase in detection sensitivity. Furthermore, the fluorescence images were converted into an RGB feature dataset and analyzed using a machine learning model, establishing a nonlinear mapping relationship between the optical response and AFB₁ concentration. The applicability of the proposed method was validated using grain samples, with predicted concentrations showing good agreement with actual concentrations. Overall, the combination of NaEuF₄-based U-TRFIA with machine learning provides a sensitive, visual sensing platform for AFB₁ detection in food safety applications.

PMID:
42944963
Bibliographic data and abstract were imported from PubMed on 28 Oct 2026.

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