Pubblicazioni

Pollen discrimination and classification by Fourier Transform Infrared (FT-IR) microspectroscopy and machine learning  (2009)

Autori:
Dell'Anna R.; Lazzeri P.; Frisanco M. ; Monti F. ;Malvezzi Campeggi F. ; Gottardini E. ;Bersani M.
Titolo:
Pollen discrimination and classification by Fourier Transform Infrared (FT-IR) microspectroscopy and machine learning
Anno:
2009
Tipologia prodotto:
Articolo in Rivista
Tipologia ANVUR:
Articolo su rivista
Nazioni degli autori:
ITALIA
Lingua:
Inglese
Formato:
A Stampa
Referee:
Nome rivista:
Analytical and Bioanalytical Chemistry
ISSN Rivista:
1618-2642
N° Volume:
394
Numero o Fascicolo:
5
Editore:
Springer Verlag
Intervallo pagine:
1443-1452
Codice PMID:
19396429
Parole chiave:
FT-IR microspectroscopy - Allergic pollen - Supervised and unsupervised learning methods - Aerobiological monitoring networks
Breve descrizione dei contenuti:
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.
Id prodotto:
53013
Handle IRIS:
11562/338127
depositato il:
21 marzo 2012
ultima modifica:
2 novembre 2016
Citazione bibliografica:
Dell'Anna R.; Lazzeri P.; Frisanco M. ; Monti F. ;Malvezzi Campeggi F. ; Gottardini E. ;Bersani M., Pollen discrimination and classification by Fourier Transform Infrared (FT-IR) microspectroscopy and machine learning «Analytical and Bioanalytical Chemistry» , vol. 394 , n. 52009pp. 1443-1452

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Titolo Dipartimento Responsabili
Sviluppo di metodologie alternative per l'identificazione e la determinazione quantitativa di pollini allergenici Dipartimento Informatica Francesca Monti
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