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FT-IR Hyperspectral Imaging and Artificial Neural Network Analysis for Identification of Pathogenic Bacteria
journal contribution
posted on 2018-06-26, 00:00 authored by Peter Lasch, Maren Stämmler, Miao Zhang, Malgorzata Baranska, Alejandra Bosch, Katarzyna MajznerIdentification
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.
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replica methodPathogenic Bacteria IdentificationANN classifiersspecies levelquality testsFT-IR Hyperspectral Imagingtaxonomic resolutionsegmentation mapsmid-IR microspectroscopic imaging systemFT-IR hyperspectral imagingcultivation stepagar plateshyperspectral image segmentationmicrocolonieIR windowsFT-IR hyperspectral imaging measurementsimage segmentationidentification techniquesguarantee single-colony growthmicrocolony imprintstransferSpectral data analysiscell suspensionArtificial Neural Network Analysis300 μ mincubation periodplane arrayapplicationIR-transparent CaF 2 windowsDry microcolony imprintsFPAcell layers
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