Abstract
Single particle aerosol mass spectrometers record two mass spectra for each individual particle, producing large amounts of data. The analysis of these spectra is typically performed using data mining algorithms like fuzzy c-means clustering. Here we present a new approach by applying the Ordering Points To Identify the Clustering Structure (OPTICS) algorithm in combination with fuzzy c-means clustering to single particle mass spectra. OPTICS treats spectra as points in n-dimensional space where each mass to charge ratio represents a dimension. The algorithm orders the spectra based on their density in this n-dimensional space.To demonstrate the strength of this combination of algorithms we applied it to an ambient dataset. The graphical representation of the results reflects chemical processing in the sampled aerosol. This allows for a detailed interpretation of the chemical evolution of the atmospheric aerosol.
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