Nanoplastic Identification With Mass Spectrometry

Currently, micro- and nanoplastics are a significant concern within the scientific community. Besides their ability to accumulate in human tissues and organs, they have the potential to cause serious diseases. Consequently, increasing attention is being paid to methods for detecting nanoplastics in human biological fluids. One such method is mass spectrometry, which allows for precise determination of the quantitative and qualitative composition of a sample, even at low concentrations. However, there are no computational algorithms and methods for the identification and annotation of nanoplastics. In this work, a Python library has been developed and implemented for the identification of polystyrene sulfonate (PSS) and perfluoroalkyl (PFAS) nanoplastics using mass spectrometry. The library's functionality includes the creation of a database that can be populated with experimental spectra and generated ions. Using the algorithm developed in this work, the library generates \emph{in silico} PSS and PFAS ions, taking into account their chemical properties and possible structural variations. These ions are automatically added to the database. For the analysis of experimental data, the library provides tools for reading experimental mass spectra: the library's functions allow for the reading, cleaning, and annotating of experimental spectra, conducting preliminary validation of results using a target-decoy approach. The library also includes a data visualization function, enabling users to graphically represent the analysis results. This library serves as a valuable tool for scientists and researchers. The ion generation algorithm is crucial for establishing a foundation for a database-searching approach in the future. The library significantly simplifies the data analysis process, making it more automated, and provides a convenient means for visualizing and interpreting the results.