An algorithm for automatic fitting and formula assignment in atmospheric mass spectra

Abstract. Mass spectrometry is an established method for studying the chemical composition of gases and particles in the atmosphere. Using this technique, signals corresponding to thousands or even tens of thousands of compounds may be detected from ambient air. The process of identifying all the peaks in the mass spectra is often arduous and time-consuming, in particular when multiple overlapping peaks are present. This manual peak fitting and identification may take even experienced analysts anywhere from weeks to months to complete, depending on the desired accuracy and completeness. In this work, we attempted to automate the fitting and formula assignment workflow and evaluate how far the process can get using a “one-button” algorithm. The algorithm constructed in this work takes in commonly known parameters specific to the instrument type, and by pressing one button it runs and ultimately provides a list of likely peaks for the mass spectrum. The algorithm utilizes weighted-least-squares fitting and a modified version of the Bayesian information criterion along with an iterative formula assignment process. We applied it to synthetic mass spectra and both a gas-phase chemical-ionization mass spectrometer (CIMS) dataset and an aerosol mass spectrometer (AMS) dataset. The results were largely comparable with manual peak fitting and identification done previously but were achieved in a fraction of the time. Erroneous assignments mainly appeared at low-intensity signals, with interference from nearby higher-intensity signals, a case that is challenging also for manual peak fitting. This algorithm provides an excellent starting point for a peak list, which, if needed, can be manually revised. The main result of this study is the algorithm itself. While further improvements and tweaks are possible, the algorithm presented here is currently being implemented into the commonly used Tofware analysis software package to allow easy utilization by the broader community. We hope this can save valuable time of researchers for data interpretation rather than data processing and curation.