Dataset from Broad-Spectrum Detection of VOC and Non-VOC Biomarkers from Patient Exhalant Using a Biomimetic Multiplexed eNose Biosensor for COVID-19 Diagnosis

Background: This study developed biomimetic olfaction- and gustation-based sensors for detecting volatile organic compound (VOC) biomarkers against SARS-CoV-2.

Materials/Methods: Computational design was used to deduce sets of odorant binding peptide (OBP) sequences that selectively bind to exhaled VOCs associated with SARS-CoV-2 from libraries of olfactory proteins. Chimeric peptides including these OBP sequence and an aromatic carbon-binding group were then synthesized, validated for vapor phase VOC binding, and then bound on to separate low-cost graphene and carbon nanotube sensor field effect transistors in multiplexed arrays that were developed in this study.

Outcome/Impact: The exhalant or simulated exhalant response data from the multiplexed OBP-functionalized sensor arrays were used to train an AI-driven diagnosis system to create a rapid, easily scalable test for SARS-CoV-2 based on direct exhaled breath.