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Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopy and machine learning

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Abstract

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 × 25 μm2 area or from a group of grains inside a 100 × 100 μm2 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.

Traditional and innovative methods for the identification of airborne pollen grains

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Acknowledgments

The authors would like to thank Antonella Cristofori and Fabiana Cristofolini for their precious contribution to selection and collection of pollen samples. This study has been granted by the FONDAZIONE CASSA DI RISPARMIO DI TRENTO E ROVERETO through the CARPOL biennial (2006–2008) project. The FT-IR instrument used in the present work was acquired by Dipartimento di Informatica-Università degli Studi di Verona with the financial support of Fondazione Cariverona.

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Authors and Affiliations

  1. Center for Materials and Microsystems, Fondazione Bruno Kessler, Via Sommarive 18, 38100, Trento, Italy

    R. Dell’Anna, P. Lazzeri & M. Bersani

  2. Fondazione Bruno Kessler & CNR Istituto di Biofisica, Via alla Cascata 56/C, 38100, Trento, Italy

    M. Frisanco

  3. Dipartimento di Informatica, Università degli Studi di Verona, Strada Le Grazie 15, 37134, Verona, Italy

    F. Monti & F. Malvezzi Campeggi

  4. Area Ambiente, FEM-Centro Ricerca ed Innovazione, Via E.Mach 1, 38010, San Michele all’Adige, Trento, Italy

    E. Gottardini

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  1. R. Dell’Anna
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  2. P. Lazzeri
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  3. M. Frisanco
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  4. F. Monti
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  5. F. Malvezzi Campeggi
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  6. E. Gottardini
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  7. M. Bersani
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Correspondence to R. Dell’Anna.

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Dell’Anna, R., Lazzeri, P., Frisanco, M. et al. Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopy and machine learning. Anal Bioanal Chem 394, 1443–1452 (2009). https://doi.org/10.1007/s00216-009-2794-9

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  • DOI: https://doi.org/10.1007/s00216-009-2794-9

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