| Popis: |
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, machine learning classifiers are created using a dataset 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. Classification models were also created to differentiate aerosol within four broad categories: fertile soils, mineral/metallic particles, biological, 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 %. Classification of aerosols by specific type resulted in a classification accuracy of ~ 87 %. The ‘trained’ model was then applied to a ‘blind’ mixture of aerosols which was known to 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. |
