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Multimodal LFIA platform integrating visual readout, smartphone grayscale imaging and surface-enhanced Raman scattering for point-of-care detection of SARS-CoV-2 protein.

Created on 09 Nov 2026

Authors

Qingling Tang, Zhangying Jiang, Duo Lin

Published in

Analytical methods : advancing methods and applications. Nov 09, 2026. Epub Nov 09, 2026.

Abstract

Rapid, simple, and sensitive antigen detection remains important for point-of-care screening and respiratory infection surveillance. Conventional lateral flow immunoassays (LFIAs) enable rapid visual testing, but their sensitivity is often limited for low-abundance viral antigens. Herein, we developed a multimodal LFIA platform for SARS-CoV-2 nucleocapsid (N) protein detection in simulated saliva. The platform integrates three complementary readout modes on a single strip: naked-eye visual observation for rapid qualitative screening, smartphone grayscale imaging for digital recording and semi-quantitative comparison of test-line color intensity, and surface-enhanced Raman scattering (SERS) measurement for sensitive spectral quantification. Citrate-capped gold nanoparticles were labeled with 4-mercaptobenzoic acid and conjugated with anti-SARS-CoV-2 N protein detection antibodies to construct Raman-active immunoprobes. The calculated analytical limit of detection (LOD) was 5.2 pg mL-1, whereas 50 pg mL-1 was the lowest experimentally tested concentration that produced a clearly distinguishable SERS response. The lowest experimentally distinguishable concentration for smartphone grayscale analysis was 160 pg mL-1. The naked-eye visual threshold was approximately 200 pg mL-1. In simulated saliva samples, recoveries ranged from 87.70% to 103.70%, with relative standard deviations of 4.8-7.1%. These results demonstrate that the proposed multimodal LFIA strategy preserves the simplicity of conventional strips while enabling digital interpretation and sensitive low-concentration antigen detection in saliva-like matrices.

PMID:
43002132
Bibliographic data and abstract were imported from PubMed on 09 Nov 2026.

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