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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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Machine Learning Integrated Near-Infrared Surface-Enhanced Raman Spectroscopy for Accurate Strain-Level Virus

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    This study introduces an AI-powered near-infrared surface-enhanced Raman spectroscopy (NIR-SERS) platform for rapid virus identification. The novel system accurately distinguishes viral strains, enhancing global public health preparedness.

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    Area of Science:

    • Nanotechnology
    • Spectroscopy
    • Artificial Intelligence

    Background:

    • Strain-level virus identification is crucial for public health but current methods lack speed and sensitivity.
    • Surface-enhanced Raman spectroscopy (SERS) offers potential but faces limitations with clinical samples and near-infrared (NIR) excitation.
    • Existing diagnostic tools struggle with spectral complexity and generalizability in artificial intelligence (AI) applications.

    Purpose of the Study:

    • To develop an AI-empowered NIR-SERS platform for rapid and accurate virus identification.
    • To overcome limitations of conventional SERS and AI diagnostics in complex clinical samples.
    • To enable precise classification of viruses, including challenging strain-level differentiation.

    Main Methods:

    • Integration of machine learning with a hybrid substrate of gold nanostars (AuNSt) and gold-coated carbon nanotube arrays (AuCNT).
    • Utilizing a rationally designed substrate to generate localized plasmonic hot spots resonant to NIR excitation.
    • Employing electron energy-loss spectroscopy (EELS) to confirm substrate performance and signal amplification.

    Main Results:

    • The platform achieved accurate classification of respiratory viruses (influenza and coronaviruses) at type, subtype, and strain levels.
    • Demonstrated effective signal amplification from viral components using NIR-SERS.
    • Overcame plasmonic mismatch issues common in conventional SERS techniques.

    Conclusions:

    • The AI-empowered NIR-SERS platform shows significant promise for enhancing rapid virus detection and identification.
    • This approach addresses critical challenges in global public health preparedness by improving outbreak response capabilities.
    • The system offers a generalizable diagnostic solution for identifying novel viral strains and subtypes.