On the application of multi-parametric optical phenotyping of bacterial colonies for multipurpose microbiological diagnostics

Igor Buzalewicz; Magdalena Karwańska; Alina Wieliczko; Halina Podbielska

doi:10.1016/j.bios.2020.112761

On the application of multi-parametric optical phenotyping of bacterial colonies for multipurpose microbiological diagnostics

Biosens Bioelectron. 2021 Jan 15:172:112761. doi: 10.1016/j.bios.2020.112761. Epub 2020 Oct 24.

Authors

Igor Buzalewicz¹, Magdalena Karwańska², Alina Wieliczko², Halina Podbielska³

Affiliations

¹ Bio-Optics Group, Department of Biomedical Engineering, Wroclaw University of Science and Technology, 27 Wybrzeze S. Wyspianskiego St., 50-370, Wroclaw, Poland. Electronic address: igor.buzalewicz@pwr.edu.pl.
² Department of Epizootiology and Veterinary Administration with Clinic of Infectious Diseases, Wroclaw University of Environmental and Life Science, 45 Grunwaldzki Square, 50-366, Wroclaw, Poland.
³ Bio-Optics Group, Department of Biomedical Engineering, Wroclaw University of Science and Technology, 27 Wybrzeze S. Wyspianskiego St., 50-370, Wroclaw, Poland.

PMID: 33129071
DOI: 10.1016/j.bios.2020.112761

Abstract

The development of new diagnostics techniques and modalities is critical for early detection of microbial contamination. In this study, the novel integrated system for multi-parametric optical phenotyping and characterization of bacterial colonies, is presented. The system combines Mach-Zehnder interferometer with a spectral imaging system for capturing multispectral diffraction patterns and multispectral two-dimensional transmission maps of bacterial colonies, along with the simultaneous interferometric profilometry. The herein presented investigation was carried out on five representative bacteria species and nearly 3000 registered multispectral optical signatures. The interferograms were analyzed by four-step phase shift algorithm to reconstruct the colony profile to enable the obtaining of the comparable optical signatures. The dedicated image processing algorithms were used for extraction of quantitative features of these signatures. The random forest algorithm was applied for selection of the most predictive set of features, which were used in classification model based on Support-Vector Machine. Obtained results have shown that the use of multiple multispectral optical signatures provide a multi-parametric bacteria identification at an exceptionally high accuracy (99.4-100%), significantly better than in case of classification based on each of these signatures (multispectral diffraction patterns, two-dimensional transmission coefficient maps), separately. Obtained results revealed that analysis of multispectral signatures can also be applied for characterisation of physical, physicochemical and chemical properties of the bacterial colonies in the presence of the antimicrobial factors. Therefore, the proposed label-free, non-destructive optical technique has perspectives to be exploited in the multipurpose diagnostics and it can be used as a pre-screening tool in microbiological laboratories.

Keywords: Bacteria identification; Classification; Interferometry; Multispectral imaging; Optical sensing; Pattern recognition.

MeSH terms

Algorithms
Bacteria
Biosensing Techniques*
Image Processing, Computer-Assisted