The discrimination and classification of allergy-relevant pollen was studied for the first time by mid-infrared Fourier transform infrared (FT-IR) microspectroscopy together with unsupervised and supervised multivariate statistical methods. Pollen samples of 11 different taxa were collected, whose outdoor air concentration during the flowering time is typically measured by aerobiological monitoring networks. Unsupervised hierarchical cluster analysis provided valuable information about the reproducibility of FT-IR spectra of the same taxon acquired either from one pollen grain in a 25 x 25 microm2 area or from a group of grains inside a 100 x 100 microm2 area. As regards the supervised learning method, best results were achieved using a K nearest neighbors classifier and the leave-one-out cross-validation procedure on the dataset composed of single pollen grain spectra (overall accuracy 84%). FT-IR microspectroscopy is therefore a reliable method for discrimination and classification of allergenic pollen. The limits of its practical application to the monitoring performed in the aerobiological stations were also discussed.

Airborne pollen are largely studied to obtain information about the atmospheric content of natural allergens. Aerobiological monitoring networks have been established to provide reliable data that facilitate the timely initiation of preventive actions aimed at minimizing allergic symptoms. Airborne pollen are usually identified and counted using an optical microscope, but as such procedures are extremely time-consuming, more expedient options are being explored. We have assessed the potential of Fourier transform infrared (FT-IR) spectroscopy as an alternative method for the rapid and reliable identification of allergenic pollen using six well-known allergenic pollen taxa and obtaining the respective FT-IR spectra. In doing this, a first IR spectral library has been created. The spectra of unknown pollen were compared to those of the reference library, and two pollen taxa of a mixed sample were identified.

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