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

We are developing a method for the real-time analysis of airborne micropart icles based on laser-ablation mass spectroscopy. Airborne particles enter a n ion trap mass spectrometer through a differentially pumped inlet, are det ected by light scattered from two continuous-wave (CW) laser beams, and sam pled by a 10-ns excimer laser pulse at 308 nm as they pass through the cent er of the ion trap electrodes. Following the laser pulse the stored ions ar e mass analyzed with the use of conventional ion trap methods. In this work thousands of positive and negative ion spectra were collected for 18 diffe rent samples: six species of bacteria, six types of pollen, and six types o f particulate matter. The data were averaged and analyzed with the use of t he multivariate patch algorithm (MPA), a variant of traditional multivariat e analysis. The MPA successfully differentiated between all of the average positive ion spectra and 17 of the 18 average negative ion spectra. In addi tion, when the average positive and negative spectra were combined the MPA correctly identified all 18 types of particles. Finally, the MPA is also ab le to identify the components of computer-synthesized mixtures of spectra f rom the samples studied. These results demonstrate the feasibility of using a less-specific real-time analytical monitoring technique to detect substa ntial changes in the background concentration of environmental organisms, i ndicating that a more selective assay should be initiated. (C) 2000 John Wi ley & Sons, Inc.