Virus Detection Method Uses Spectroscopy, AI

Researchers have developed a highly accurate and sensitive virus detection method using Raman spectroscopy, a portable virus capture device, and machine learning. The device could enable real-time virus detection and identification to help battle future pandemics.

The new virus detection method is label-free and not aimed at any specific virus, thus enabling researchers to identify potential new strains of viruses. It is also rapid and suitable for fast screening in crowded public spaces. In addition, the rich Raman features, together with machine learning analysis, enable a deeper understanding of the virus structures.

Raman spectroscopy detects unique vibrations in molecules by picking up shifts when a laser light beam induces these vibrations. A microfluidic device traps viruses between forests of aligned carbon nanotubes to capture the viruses. Such a device could use virus cultures, saliva, nasal washes, or even exhaled breath, including samples, gathered on-site during an outbreak. The carbon nanotube forests would filter out any foreign substance or background molecules from the host or surrounding air that could make it more challenging to get an accurate reading.

Once the samples are captured and the Raman microscope examines them, the machine learning aspect comes into play. The researchers gathered the Raman spectra of three different categories of viruses: human respiratory viruses, avian viruses, and enteroviruses. This data is then used to train a machine learning model, a convolutional neural network, to identify viruses.

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