Pyrolysis gas chromatography differential mobility spectrometry analysis of viruses

Pathogens present in the environment, present in a naturally-occurring global pandemic or released in an act of bioterrorism have been a recent cause of concern due to the potential for large loss of human life. Quick identification of pathogens in environmental or clinical samples is essential to implementing control measures to limit the effects of any of these possibilities. Many current pathogen detection methods do not have a fast enough response time, are not specific for a variety of infectious agents, nor are they portable for easy deployment in the field.;Pyrolysis gas chromatography differential mobility spectrometry (Py/GC/DMS) and pyrolysis gas chromatography mass spectrometry (Py/GC/MS) are two analytical methods that can be used in the identification of viruses in complex samples. Of the two, Py/GC/MS has been used much more widely in the past for identifying viruses as well as other biological sample types, and there is a large library relating chemical composition to data output. The advantage in Py/GC/DMS lies in its quick scan time and its ability to be miniaturized and robustness of the system for field usage. This study explores the application of Py/GC/DMS coupled with machine learning algorithms for the identification of viruses. Specifically, we use two common bacteriophages T2 and T4 which are easily obtained and commonly used as surrogates for optimizing detectors for mammalian viruses. Py/GC/MS is also used to allow for identification of virus fragments, and to correlate with the PY/GC/DMS results.