Articles | Volume 10, issue 4
Research article
04 Apr 2017
Research article |  | 04 Apr 2017

FATES: a flexible analysis toolkit for the exploration of single-particle mass spectrometer data

Camille M. Sultana, Gavin C. Cornwell, Paul Rodriguez, and Kimberly A. Prather

Abstract. Single-particle mass spectrometer (SPMS) analysis of aerosols has become increasingly popular since its invention in the 1990s. Today many iterations of commercial and lab-built SPMSs are in use worldwide. However, supporting analysis toolkits for these powerful instruments are outdated, have limited functionality, or are versions that are not available to the scientific community at large. In an effort to advance this field and allow better communication and collaboration between scientists, we have developed FATES (Flexible Analysis Toolkit for the Exploration of SPMS data), a MATLAB toolkit easily extensible to an array of SPMS designs and data formats. FATES was developed to minimize the computational demands of working with large data sets while still allowing easy maintenance, modification, and utilization by novice programmers. FATES permits scientists to explore, without constraint, complex SPMS data with simple scripts in a language popular for scientific numerical analysis. In addition FATES contains an array of data visualization graphic user interfaces (GUIs) which can aid both novice and expert users in calibration of raw data; exploration of the dependence of mass spectral characteristics on size, time, and peak intensity; and investigations of clustered data sets.

Short summary
Single-particle mass spectrometers (SPMSs) can determine the size and chemical composition of single particles in real time. We developed the first open-source SPMS toolkit to allow creative script-based data mining along with GUI-based visual data exploration and calibration all within a single programming environment. We believe that this software will be adopted by many in the SPMS community and improve the efficiency of knowledge discovery from these atmospherically critical data sets.