UNT Professor Drives Innovation in Breath Analysis, Disease Diagnosis, and So Much More


Guido Verbeck

The tech space has seen a significant market development with sensors. These devices are typically integrated with machine learning and artificial intelligence to process mass amounts of data and provide inputs to further develop autonomous systems. Sensors have many applications and use-cases in most, if not all, sectors as the digital world grows. The value and quality of data are determined by its source and collection method, hence the importance of quality sensor development.


University of North Texas (UNT) professor Guido Verbeck and his team at the university are innovating the utility of chemical and biological sensors through a collaboration with Swiss-based Inficon. Verbeck is an expert on innovative uses of mass spectrometry.


The developments of Verbeck and the team can be applied in a variety of instances, such as:


- A drone that flies over chemical fires

- Detecting toxins in Delta-8, a derivative of CBD

- Covid-19 breathalyzer test

- Diagnosing diseases with a sensor


In 2020, Verbeck and the UNT team developed their chemical sensor and partnered with Dallas-based Worlds Inc. to turn the sensor into a rapid COVID-19 breathalyzer test. Various diseases have their own “markers,” meaning they give off unique volatile organic compounds (VOCs) that are essentially a fingerprint of a specific condition or, in this case, the COVID-19 virus.


This technology was initially developed to identify chemicals in the air, such as fumes from fires, narcotics, or mass graves. Now, it has the potential to shake up medical diagnoses, according to Verbeck, by identifying early-stage cancer markers and metabolic disorders in real-time.


“Breath analysis is starting to trend as a diagnosis tool for a myriad of disease states. Breast cancer, diabetes, and lung cancer have already been determined to have their own breath markers. Creating a device that can look for not only respiratory illnesses but also early-stage cancer markers and metabolic disorders in real-time could really change the diagnosis field. Because of this large application set, it was important to apply AI and machine learning to the problem.”


Click here for the original article by Dallas Innovates.