# COVID-19 detection through scent analysis with a compact GC device

> **NIH NIH U18** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $999,775

## Abstract

Recent studies, including ours, have suggested that breath may allow us to diagnose COVID-19 infection
and even monitor its progress. As compared to immunological and genetic based methods using sample media
like blood, nasopharyngeal swab, and saliva, breath analysis is non-invasive, simple, safe, and inexpensive; it
allows a nearly infinite amount of sample volume and can be used at the point-of-care for rapid detection.
Fundamentally, breath also provides critical metabolomics information regarding how human body responds to
virus infection and medical intervention (such as drug treatment and mechanical ventilation). The objectives of
the proposed SCENT project are: (1) to refine automated, portable, high-performance micro-gas
chromatography (GC) device and related data analysis / biomarker identification algorithms for rapid (5-6
minutes), in-situ, and sensitive (down to ppt) breath analysis and (2) to conduct breath analysis on up to 760
patients, and identify and validate the COVID-19 biomarkers in breath. Thus, in coordination with the RADx-rad
Data Coordination Center (DCC), we will complete the following specific aims.
(1) Refine 5 automated micro-GC devices to achieve higher speed and better separation capability. We
will construct 5 new automated and portable one-dimensional micro-GC devices that require only ~6 minutes of
assay time (improved from current 20 minutes) at the ppt level sensitivity (Sub-Aim 1a). Then the devices will be
upgraded to 2-dimensional micro-GC to significantly increase the separation capability (Sub-Aim 1b). In the
meantime, we will optimize and automate our existing data processing and biomarker identification algorithms
and codes to streamline the workflow so that the GC device can automatically process and analyze the data
without human intervention (Sub-Aim 1c).
(2) Identify breath biomarkers that distinguish COVID-19 positive (symptomatic and asymptomatic) and
negative patients. We will recruit a training cohort of 380 participants, including 190 COVID-19 positive patients
(95 symptomatic and 95 asymptomatic) and 190 COVID-19 negative patients from two hospitals (Michigan
Medicine – Ann Arbor and the Henry Ford Hospital – Detroit). We will conduct breath analysis using machine
learning to identify VOC patterns that match each COVID-19 diagnostic status.
(3) Validate the COVID-19 biomarkers using our refined micro-GC devices. Using the refined 2-D micro-GC
devices from Sub-Aim 1b, we will recruit a new validation cohort of 380 participants (190 COVID-19 positive
patients and 190 COVID-19 negative patients) to validate the biomarkers identified in Aim 2.
 We will leverage existing engineering, data science, clinical, regulatory, and commercialization resources
throughout the project to hit our milestones, ensuring a high likelihood of rapid patient impact. Upon completion
of this work, we will have a portable micro-GC device and accompanying automated algorithms that can detect
and monitor COVID-19 status f...

## Key facts

- **NIH application ID:** 10266206
- **Project number:** 1U18TR003812-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Xudong Fan
- **Activity code:** U18 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $999,775
- **Award type:** 1
- **Project period:** 2020-12-21 → 2022-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10266206

## Citation

> US National Institutes of Health, RePORTER application 10266206, COVID-19 detection through scent analysis with a compact GC device (1U18TR003812-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10266206. Licensed CC0.

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