A machine learning approach to aerosol classification for single-particle mass spectrometry

<p>Compositional analysis of atmospheric and laboratory aerosols is often conducted via single-particle mass spectrometry (SPMS), an in situ and real-time analytical technique that produces mass spectra on a single-particle basis. In this study, classifiers are created using a data set of SPMS spectra to automatically differentiate particles on the basis of chemistry and size. Machine learning algorithms build a predictive model from a training set for which the aerosol type associated with each mass spectrum is known a priori. Our primary focus surrounds the growing of random forests using feature selection to reduce dimensionality and the evaluation of trained models with confusion matrices. In addition to classifying  ∼ 20 unique, but chemically similar, aerosol types, models were also created to differentiate aerosol within four broader categories: fertile soils, mineral/metallic particles, biological particles, and all other aerosols. Differentiation was accomplished using  ∼ 40 positive and negative spectral features. For the broad categorization, machine learning resulted in a classification accuracy of  ∼ 93&thinsp;%. Classification of aerosols by specific type resulted in a classification accuracy of  ∼ 87&thinsp;%. The <q>trained</q> model was then applied to a <q>blind</q> mixture of aerosols which was known to be a subset of the training set. Model agreement was found on the presence of secondary organic aerosol, coated and uncoated mineral dust, and fertile soil.</p>