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Anal Chem. 2018 Aug 7;90(15):8896-8904. doi: 10.1021/acs.analchem.8b01024. Epub 2018 Jul 11.

FT-IR Hyperspectral Imaging and Artificial Neural Network Analysis for Identification of Pathogenic Bacteria.

Author information

1
ZBS6 Proteomics and Spectroscopy , Robert Koch-Institute , Seestrasse 10 , Berlin , D-13353 , Germany.
2
Faculty of Chemistry , Jagiellonian University , Gronostajowa 2 , 30-060 Krakow , Poland.
3
CINDEFI, CONICET-CCT La Plata, Facultad de Ciencias Exactas , Universidad Nacional de La Plata , 1900 La Plata , Buenos Aires , Argentina.

Abstract

Identification of microorganisms by Fourier transform infrared (FT-IR) spectroscopy is known as a promising alternative to conventional identification techniques in clinical, food, and environmental microbiology. In this study we demonstrate the application of FT-IR hyperspectral imaging for rapid, objective, and cost-effective diagnosis of pathogenic bacteria. The proposed method involves a relatively short cultivation step under standardized conditions, transfer of the microbial material onto suitable IR windows by a replica method, FT-IR hyperspectral imaging measurements, and image segmentation by machine learning classifiers, a hierarchy of specifically optimized artificial neural networks (ANN). For cultivation, aliquots of the initial microbial cell suspension were diluted to guarantee single-colony growth on solid agar plates. After a short incubation period when microbial microcolonies achieved diameters between 50 and 300 μm, microcolony imprints were produced by using a specifically developed stamping device which allowed spatially accurate transfer of the microcolonies' upper cell layers onto IR-transparent CaF2 windows. Dry microcolony imprints were subsequently characterized using a mid-IR microspectroscopic imaging system equipped with a focal plane array (FPA) detector. Spectral data analysis involved preprocessing, quality tests, and the application of supervised modular ANN classifiers for hyperspectral image segmentation. The resulting easily interpretable segmentation maps suggest a taxonomic resolution below the species level.

PMID:
29944341
DOI:
10.1021/acs.analchem.8b01024
[Indexed for MEDLINE]

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