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Related Experiment Video

Updated: Jan 10, 2026

Development of a Lateral Flow Immunochromatographic Strip for Rapid and Quantitative Detection of Small Molecule Compounds
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Full-Spectrum Analysis With Machine Learning for Quantitative Assessment of Lateral Flow Immunoassays: A Platform

Cheng-Han Chen, Yi-Tzu Lee, Chitsung Hong

    IEEE Transactions on Bio-Medical Engineering
    |November 24, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study enhances qualitative lateral flow immunoassays (LFIA) with semi-quantitative capabilities using spectral analysis and machine learning. The platform provides rapid, semi-quantitative results for diagnostics, especially in resource-limited settings.

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    Area of Science:

    • Analytical Chemistry
    • Biomedical Engineering
    • Data Science

    Background:

    • Lateral flow immunoassays (LFIA) offer rapid point-of-care diagnostics but are typically qualitative.
    • There is a need to enhance LFIA capabilities for semi-quantitative analysis to improve diagnostic utility.

    Purpose of the Study:

    • To develop and validate a platform technology integrating full-spectrum analysis with machine learning for semi-quantitative assessment of qualitative LFIA.
    • To demonstrate the adaptability of this framework for various analytes beyond infectious diseases.

    Main Methods:

    • Portable spectrometry was used to analyze gold nanoparticle optical signatures from SARS-CoV-2 rapid tests on 241 clinical nasopharyngeal specimens.
    • Signal processing involved Savitzky-Golay filtering, standard normal variate transformation, and principal component analysis for dimensionality reduction.
    • Machine learning algorithms, particularly random forest, were employed to correlate spectral data with quantitative measures (PCR Ct values).

    Main Results:

    • The T-C differential normalization strategy outperformed the T/C ratio.
    • Principal component analysis reduced spectral data dimensionality while retaining significant variance (97.26%).
    • Random forest achieved high performance (R² = 0.961, RMSE = 2.235 Ct) for semi-quantitative assessment within a clinically relevant range, with a measurement uncertainty of ±4.2 Ct.

    Conclusions:

    • Standard LFIA tests contain extractable semi-quantitative information through spectral-machine learning integration.
    • This platform enhances LFIA utility, offering valuable semi-quantitative results for population surveillance and in resource-limited settings.
    • The modular design allows for adaptation to diverse analytes, establishing a foundation for advanced lateral flow diagnostics.