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

BOSCH, ALEJANDRA; STÄMMLER, MAREN; BARANSKA, MALGORZATA; LASCH, PETER; MAJZNER, KATARZYNA; ZHANG, MIAO

ANALYTICAL CHEMISTRY

AMER CHEMICAL SOC

Lugar: Washington, DC 20036; Año: 2018 vol. 1 p. 1 - 19

0003-2700

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 micro-colonies achieved diameters between 50 and 300 µm, micro-colony imprints were produced by using a specifically developed stamping device which allowed spatially accurate transfer of the micro-colonies? upper cell layers onto IR transparent CaF2 windows. Dry micro-colony imprints were subsequently characterized using a mid-IR microspectroscopic imaging system equipped with a focal plane array (FPA) detector. Spectral data analysis involved pre-processing, 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.