# Portable GC detector for breath-based COVID diagnostics

> **NIH NIH U18** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2021 · $975,463

## Abstract

Project Summary/Abstract: This proposal has two major goals: 1) Define signature exhaled breath volatile
organic compounds (VOCs) to diagnose SARS-CoV-2 infections, and 2) Develop a portable chemical sensing
device that can capture and detect exhaled VOCs and includes machine learning algorithms for automated
data processing and results interpretation. This project will bring a portable sensor forward into clinical use with
the aim of supplementing COVID-19 diagnostics with a reagentless alternative. Breath testing of exhaled VOC
biomarkers is a relatively new concept that has the potential to transform healthcare in the US and globally.
Our overarching hypothesis is that a miniature breath analysis device can measure signatures of exhaled
breath VOCs in real-time and correlate their profile to viral upper respiratory infections such as SARS-CoV-2,
even asymptomatically. In Aim #1, we propose a prospective, observational study to analyze breath samples
from COVID-19 positive and negative subjects, solely for the purpose of analysis through gold standard GC-
MS to define breath VOC biomarkers of infection. We will recruit subjects at two local sites, the UC Davis
Medical Center (Sacramento, CA) and VA Northern California Health Care System (Mather, CA), where MPI
Dr. Kenyon and Co-Is Drs. Harper and Schivo have joint clinical appointments. Our group has a proven track
record to conduct these types of clinical breath studies. In Aim #2, we will develop a portable breath analysis
device using our novel miniature differential mobility spectrometry (DMS) detector, coupled with chip-based
gas chromatography. DMS is a subset of ion mobility spectrometry and detects VOCs at ambient temperatures
and pressures, making it highly appropriate for portable devices. This device would include our custom chip-
based preconcentrator, which is packed with a chemical sorbent for extraction of VOCs from breath, and will
compare functionality of a compact commercially available GC column to a micro-GC column chip from
Deviant, a subcontractor in this work. Individual components of this device have already been developed, and
under direction of MPI Prof. Davis, Chair of Mechanical and Aerospace Engineering, a team of research
engineers would integrate these pieces together into a single unit. Collaborator Prof. Chuah would guide
development of a custom software package for the device with machine learning and artificial intelligence
capabilities for automated data processing and interpretation. The device would be placed in the hands of
clinicians, who would provide feedback that engineers would immediately incorporate into the device and
return to the clinicians for more testing. Under Aim #3, our team would process the GC-MS and GC-DMS data
generated in this work, identifying a novel VOC profile for COVID-19 diagnostics. Aim #4 would initiate towards
the end of this study to develop both a regulatory pathway & contract manufacturing plan for large scale
production an...

## Key facts

- **NIH application ID:** 10266337
- **Project number:** 1U18TR003795-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** CRISTINA ELIZABETH DAVIS
- **Activity code:** U18 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $975,463
- **Award type:** 1
- **Project period:** 2020-12-21 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10266337

## Citation

> US National Institutes of Health, RePORTER application 10266337, Portable GC detector for breath-based COVID diagnostics (1U18TR003795-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10266337. Licensed CC0.

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