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

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 CaF 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.

Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network

“…Hyperspectral imaging (HSI) being an emerging technique has drawn raising attentions from researchers in the eld of analytical chemistry due to the capacity of acquiring spectral and spatial information simultaneously. 6,7 This leads to a derived advantage, namely visualization of category and chemical composition distribution of samples through combining the spectrum and the corresponding spatial location of each pixel in HSI image. In addition, batch detection is another superiority, making HSI very suitable to process large-scale samples.…”

Bacterial Typing and Identification Based on Fourier Transform Infrared Spectroscopy

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Bacterial Typing and Identification Based on Fourier Transform Infrared Spectroscopy

“…ANNs can used in identification by analyzing raw data and work well for managing overlapping data because they are a pattern analysis method of advanced multivariate data processing in which large amounts of information are analyzed by training the data in a pattern recognition algorithm to recognize the particular combination of variables in a subset of data 113 . The general strategy of ANN analysis includes teaching and optimizing the network models, followed by testing the classifiers with independent (external) validation data sets 114 . Teaching and internal validation were carried out on the basis of IR spectra with known class assignment using spectra from the database.…”