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Detecting volatile organic compounds (VOCs) is increasingly recognized as a pivotal tool in non-invasive disease diagnostics. VOCs are metabolic byproducts, mostly found in human breath, urine, feces, and sweat, whose profiles may shift significantly due to pathological conditions. This paper presents a thorough review of the latest advancements in sensor technologies for VOC detection, with a focus on their healthcare applications. It begins by introducing VOC detection principles, followed by a review of the rapidly evolving technologies in this area. Special emphasis is given to functionalized molecularly imprinted polymer-based biochemical sensors for detecting breath biomarkers, owing to their exceptional selectivity. The discussion examines SWaP-C considerations alongside the respective advantages and disadvantages of VOC sensing technologies. The paper also tackles the principal challenges facing the field and concludes by outlining the current status and proposing directions for future research. 

Rapid and accurate detection of the pathogens, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for COVID-19, is critical for mitigating the COVID-19 pandemic. Current state-of-the-art pathogen tests for COVID-19 diagnosis are done in a liquid medium and take 10–30 min for rapid antigen tests and hours to days for polymerase chain reaction (PCR) tests. Herein we report novel accurate pathogen sensors, a new test method, and machine-learning algorithms for a breathalyzer platform for fast detection of SARS-CoV-2 virion particles in the aerosol in 30 s. The pathogen sensors are based on a functionalized molecularly-imprinted polymer, with the template molecules being the receptor binding domain spike proteins for different variants of SARS-CoV-2. Sensors are tested in the air and exposed for 10 s to the aerosols of various types of pathogens, including wild-type, D614G, alpha, delta, and omicron variant SARS-CoV-2, BSA (Bovine serum albumin), Middle East respiratory syndrome–related coronavirus (MERS-CoV), influenza, and wastewater samples from local sewage. Our low-cost, fast-responsive pathogen sensors yield accuracy above 99% with a limit-of-detection (LOD) better than 1 copy/μL for detecting the SARS-CoV-2 virus from the aerosol. The machine-learning algorithm supporting these sensors can accurately detect the pathogens, thereby enabling a new and unique breathalyzer platform for rapid COVID-19 tests with unprecedented speeds.