Dataset from COVID-19 Detection through Scent Analysis with a Compact Gas Chromatography Device

Background: The objectives of the SCENT project were to refine an automated, portable, high-performance micro-gas chromatography (GC) device and related data analysis and biomarker identification algorithms for rapid, in-situ, and sensitive breath analysis, and to conduct breath analysis on patients to identify and validate COVID-19 biomarkers.

Materials/Methods: The study completed three specific aims: Aim 1 refined five automated micro-GC devices to achieve higher speed and better separation capability, Aim 2 identified breath biomarkers that distinguished COVID-19 positive and negative patients, and Aim 3 validated the COVID-19 biomarkers using the refined two-dimensional micro-GC devices.

Outcome/Impact: The study successfully developed portable micro-GC devices and accompanying automated algorithms capable of detecting and monitoring COVID-19 status in both clinical and community settings. These advances demonstrated that rapid, sensitive, and automated breath analysis could identify COVID-19 biomarkers and provide diagnostic and monitoring capabilities for diverse patient populations.