Sensor array and gas chromatographic detection of the blood serum volatolomic signature of COVID-19

Yolande Ketchanji Mougang; Lorena Di Zazzo; Marilena Minieri; Rosamaria Capuano; Alexandro Catini; Jacopo Maria Legramante; Roberto Paolesse; Sergio Bernardini; Corrado Di Natale

doi:10.1016/j.isci.2021.102851

Sensor array and gas chromatographic detection of the blood serum volatolomic signature of COVID-19

iScience. 2021 Aug 20;24(8):102851. doi: 10.1016/j.isci.2021.102851. Epub 2021 Jul 10.

Authors

Yolande Ketchanji Mougang¹, Lorena Di Zazzo², Marilena Minieri³, Rosamaria Capuano¹, Alexandro Catini¹, Jacopo Maria Legramante⁴, Roberto Paolesse², Sergio Bernardini^{3

5}, Corrado Di Natale¹

Affiliations

¹ Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, 00133 Rome, Italy.
² Department of Chemical Science and Technology, University of Rome Tor Vergata, Via della Ricerca Scientifica, 00133 Rome, Italy.
³ Department of Experimental Medicine, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy.
⁴ Department of Medicine's Systems, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy.
⁵ Emerging Technologies Division of International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milano, Italy.

Abstract

Volatolomics is gaining consideration as a viable approach to diagnose several diseases, and it also shows promising results to discriminate COVID-19 patients via breath analysis. This paper extends the study of the relationship between volatile compounds (VOCs) and COVID-19 to blood serum. Blood samples were collected from subjects recruited at the emergency department of a large public hospital. The VOCs were analyzed with a gas chromatography mass spectrometer (GC/MS). GC/MS data show that in more than 100 different VOCs, the pattern of abundances of 17 compounds identifies COVID-19 from non-COVID with an accuracy of 89% (sensitivity 94% and specificity 83%). GC/MS analysis was complemented by an array of gas sensors whose data achieved an accuracy of 89% (sensitivity 94% and specificity 80%).

Keywords: Biotechnology; Diagnostics; Health sciences; Medical biotechnology.