User Guide#
Statement of Need#
The purpose of py-tofspec is to make it easier to process and label PTR-TOF mass spectrometry data.
PTR-TOF-MS is the current state-of-the-art method for real-time monitoring of volatile organic compounds
(VOCs). The raw datasets generated by PTR-TOF-MS instruments are extremely high-dimensional matrices. They
consist of ion count values in ultrafine m/z bins, usually output at a resolution of a second or less.
The richness of this dataset provides us measurements of countless VOCs, but to disentangle the matrix and
understand what exactly is going on in the sample is no small feat. We provide functionality
for this purpose. Using a few simple steps, py-tofspec transforms raw PTR-TOF-MS data into an
integrated, labeled time-series of functional groups / substructures that can be analyzed and compared against
one another. Key ratios and new metrics emerge naturally between functional groups during an experiment.
py-tofspec delivers not only a useful summary of the chemistry within a PTR-TOF-MS sample but also a
labeled dataset that may be used for building machine learning models and making powerful visualizations.
We hope that py-tofspec will be leveraged across disciplines such as atmospheric chemistry,
soil science, metabolomics, and public health.