Analysis of Allergenic Pollen by FTIR Microspectroscopy

Fourier transform infrared (FTIR) spectroscopy is a powerful tool for the identification and characterization of pollen and spores. However, interpretation and multivariate analysis of infrared microscopy spectra of single pollen grains are hampered by Mie-type scattering. In this paper, we introduce a novel sampling setup for infrared microspectroscopy of pollens preventing strong Mie-type scattering. Pollen samples were embedded in a soft paraffin layer between two sheets of polyethylene foils without any further sample pretreatment. Single-grain infrared spectra of 13 different pollen samples, belonging to 11 species, were obtained and analyzed by the new approach and classified by sparse partial least-squares regression (PLSR). For the classification, chemical and physical information were separated by extended multiplicative signal correction and used together to build a classification model. A training set of 260 spectra and an independent test set of 130 spectra were used. Robust sparse classification models allowing the biochemical interpretation of the classification were obtained by the sparse PLSR, because only a subset of variables was retained for the analysis. With accuracy values of 95% and 98%, for the independent test set and full cross-validation respectively, the method is outperforming the previously published studies on development of an automated pollen analysis. Since the method is compatible with standard air-samplers, it can be employed with minimal modification in regular aerobiology studies. When compared with optical microscopy, which is the benchmark method in pollen analysis, the infrared microspectroscopy method offers better taxonomic resolution, as well as faster, more economical, and bias-free measurement.