Detection and classification of individual airborne microparticles using laser ablation mass spectroscopy and multivariate analysis

Abstract

We are developing a method for the real-time analysis of airborne microparticles based on laser-ablation mass spectroscopy. Airborne particles enter an ion trap mass spectrometer through a differentially pumped inlet, are detected by light scattered from two continuous-wave (CW) laser beams, and sampled by a 10-ns excimer laser pulse at 308 nm as they pass through the center of the ion trap electrodes. Following the laser pulse the stored ions are mass analyzed with the use of conventional ion trap methods. In this work thousands of positive and negative ion spectra were collected for 18 different samples: six species of bacteria, six types of pollen, and six types of particulate matter. The data were averaged and analyzed with the use of the multivariate patch algorithm (MPA), a variant of traditional multivariate analysis. The MPA successfully differentiated between all of the average positive ion spectra and 17 of the 18 average negative ion spectra. In addition, when the average positive and negative spectra were combined the MPA correctly identified all 18 types of particles. Finally, the MPA is also able to identify the components of computer-synthesized mixtures of spectra from the samples studied. These results demonstrate the feasibility of using a less-specific real-time analytical monitoring technique to detect substantial changes in the background concentration of environmental organisms, indicating that a more selective assay should be initiated. © 2000 John Wiley & Sons, Inc. Field Analyt Chem Technol 4: 31–42, 2000