20-Minute COVID-19 Test Pairs Mass Spectrometer with Machine Learning to Detect SARS-CoV-2 in Nasal Swabs

A novel COVID-19 test uses an analytical instrument known as a mass spectrometer, which is paired with a powerful machine-learning platform to detect SARS-CoV-2 in nasal swabs.

The novel method developed by researchers at UC Davis Health (Sacramento, CA, USA) has shown to be 98.3% accurate for positive COVID-19 tests and 96% for negative tests. The accuracy matches or outperforms many of the current COVID-19 screening tests. The new testing method may allow for the rapid screening of large numbers of individuals for businesses, schools, venues and other large facilities.


This is the first test for COVID-19 that pairs mass spectrometry with robotics and a robust automated machine learning platform to rapidly deliver test results. The coupling of these unique elements not only allows testing for COVID-19 but may be able to quickly adapt to detect other diseases and perhaps future pandemic organisms. The machine, a mass spectrometer MALDI-TOF, or matrix-assisted laser desorption/ionization time-of-flight, uses a laser to create small particles - ions - from large molecules in the testing sample. These ionized particles create signals that can be used to identify many compounds, including those associated with microorganisms and pathogens.

For the study, 226 nasal swabs from UC Davis’ biorepository of COVID-19 tests were ionized in the Shimadzu 8020. The swabs were from leftover samples and volunteers who consented to the study. Some of the participants had COVID-19 symptoms, and some were asymptomatic. The hundreds of peaks and signals produced by the ionized test swabs were analyzed by the automated machine learning platform MILO (Machine Intelligence Learning Optimizer). Machine learning is a subset of artificial intelligence, or AI. The platform has previously been used to predict severe infections and acute kidney disease.

For the COVID-19 test, MILO finds distinguishing patterns among the many mass spectrometry peaks and signals and deciphers which patterns correspond to the presence or absence of the SARS-CoV-2 virus in the samples. MILO accomplished the analysis in a fraction of the time that a non-automated machine-learning approach would have taken. Maurice J. Gallagher, Jr., chairman and CEO of Allegiant Travel Company, has launched a new startup, SpectraPass, to develop the rapid, automated system into a means to facilitate opening businesses and the economy. Experts at UC Davis Health are helping guide the SpectraPass team through the scientific, machine learning and clinical steps needed to move the COVID-19 testing technology closer to emergency use authorization by the Food and Drug Administration (FDA).

“The COVID-19 pandemic not only brought the world’s commerce and travel to a halt – it also took away our fundamental human interaction, our freedom to be together,” said Gallagher. “This project has resulted in a real breakthrough that can not only provide instant, accurate information about COVID infection, but can be an important part of addressing other viruses and even developing therapies. The excitement of working with the team at UC Davis is in knowing we are helping make sure our children and grandchildren are better equipped to deal with potential pandemics in the future.”

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