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Scaling Coronasensors

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The COVID-19 virus is fairly difficult to detect and currently requires expensive laboratory devices, and especially time which we mostly don’t have in this unprecedented global health emergency. Viruses are small, and although coronaviruses are fairly large due to their complexity, there’s only a handful of methods for detecting them faster. One of them is Raman spectroscopy, a long-known optical way for detecting molecular structures from a substance though which (usually laser) light is scattering.

Some of the newest Raman spectrometers are even hand-held and use inexpensive semicondutor laser leds and sensors. In the past, the accuracy and reliability were limited by laser quality and receptor sensitivity, but now these components are readily available in very high quality. A surface-enhanced Raman spectrometer produces a unique Raman shift graph specific to the molecule such as a virus. These graphs look much like our time-series graphs, but are shown in wavenumbers, with units of inverse length (cm^-1).

Now that all this is analog and optical in nature, the graph quality differs greatly based on the sample and sensor quality, outside interference and of course the sample purity. Even if the handheld device would contain the processing power of a high-end smartphone, it would still be very difficult to match and interpolate the graph data between different sensors and produce reliable matches against protosamples. This is why the problem needs to be outsourced to the cloud, which continuously and in a distributed fashion enhances the accuracy based on millions of samples sent via global mobile networks.

Orchestrating all this in a reliable, fast and cyber secure way is no easy task and requires an intricate interplay of many parties, such as operators, cloud providers, research institutions, governments and health professionals on the field. Nevertheless it is a doable thing, and we at BaseN are more than ready to do our part.

//Pasi

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